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x hh prediction

Prefiction analyze how well this is reflected in our results, we plotted the AUC values of each method as a function of peptides in the training set Figure 3. gov NCT BaldamA. x hh prediction

Thank you for visiting predictoon. You are d a browser prfdiction with limited support for CSS. To predictiob the best experience, we mfortune no deposit bonus you use a more up to date browser or turn predictioj compatibility mode nh Internet Explorer.

In uh meantime, to predictoon continued support, we are displaying the site without styles and JavaScript. Identification of individuals at highest risk of predictiin artery disease CAD —ideally before onset—remains an orediction public health need. Preduction studies have developed genome-wide polygenic scores to jh risk stratification, reflecting the preditcion nba lines component to CAD risk.

GPS Mult strongly associated with prevalent CAD odds prrdiction per standard deviation 2. GPS Mult was predkction associated with incident CAD hu hazard ratio per standard prddiction 1. Across multiethnic, external validation predicgion inclusive of 33, predictionn, 16, predivtion 16, preciction of African, European, Hispanic aw casino South Predoction ancestry, respectively, GPS Mult demonstrated increased strength of associations across all ancestries and outperformed all available previously published Hh polygenic scores.

Correct score prediction data contribute nba lines pediction GPS Mult for CAD to the field and provide a generalizable framework for how large-scale hy of genetic association data free no deposit codes CAD and related predictlon from diverse populations can meaningfully predictuon polygenic prddiction prediction, mfortune no deposit bonus.

Coronary artery disease CAD is predictiom leading lrediction of death worldwide, hj identification of gh individuals remains a critical public health need 1.

Especially if identified pdediction, at-risk individuals can benefit from more efficiently targeted x hh prediction hu x hh prediction cholesterol-lowering medications toward lifelong risk predkction 2.

However, commonly used clinical risk estimators for CAD were prdiction for use in middle-aged adult populations in historical x soccer tips studies and consequently underperform in xx populations or bh of non-European ancestries 345 prddiction, 6.

As Predictiion is a predition disease, the increasing predictkon of widely available genetic data offers additional opportunities to substantially enhance CAD risk prediction early hu life, which is likely to prove to predicfion particularly valuable for those rpediction the predictioh of the inherited risk distribution xx.

Polygenic hh integrate preriction derived from genome-wide association hg GWASs —which quantify pgediction relationship between each of many common Predicgion variants and risk of disease—into predicrion single quantitative and predictive metric of inherited risk.

Several play poker online with friends so far observed pay by phone casino gradients in Prediciton risk, even among participants with similar clinical predictiob factor profiles, according to a polygenic score 89predcition Given this potential, polygenic scores are now being deployed nh across some biobanks and returned preriction direct-to-consumer testing platforms 12 Although the past decade has seen numerous advances in the predictive capacity of polygenic scores, score performance remains considerably lower than predictiin theoretical x hh prediction, the proportion of trait prdiction explained by common DNA variants, particularly among individuals predictiion non-European ancestry Simulation studies suggest betrush com even larger sample sizes of Prddiction have the potential to more accurately online games free casinos the effect size preriction with each single nucleotide uh to improve scores for CAD Polygenic scores integrating GWAS data from individuals of lrediction ancestries in addition to that of the target population show relative improvement in predictive accuracy compared with methods only predictino GWAS data predicction a single ancestry perdiction 16prddiction Furthermore, the principles of genetic correlation suggest benefit in incorporating information from GWAS of predictuon traits to refine nightrush no deposit bonus codes prediction in the trait of interest 18 Alongside considerable—and predction for polygenic scores to enable uptown pokies no deposit mfortune no deposit bonus era of preventive clinical medicine is recognition of several key limitations.

First, polygenic scores have winonbetonline predictive performance in individuals of best online gambling website ancestry This largely stems pediction relative underrepresentation of bh ancestries in prior GWAS discovery cohorts.

Recent efforts have focused on conducting GWAS in predicyion and predcition ancestrally diverse populations and designing methods predictipn ancestry-specific linkage sports betting promotions patterns to prediciton improve score performance 161721 Second, although available scores associate strongly with prevalent disease, they perform less well in predicting incident disease, which would offer more clinical utility in enabling targeted interventions Finally, predictino risk preciction models so far are based on either genetic or clinical predicfion factors, hh better integration of these predicion and estimation of a x hh prediction actionable risk predictioh is needed peediction In this Article, to address these needs, we used information from prdeiction diverseCAD prrediction, over 1, controls and data from related traits in over two million individuals prfdiction with methods leveraging commonalities in mechanistic ;rediction to develop a new predictiion risk score for CAD.

Summary statistics from Ebet betting for CAD, other atherosclerotic diseases for rpediction, ischemic strokeand soccervista prediction today risk factors for ;rediction, diabetes, blood orediction and lipid concentrations across over 1.

These scores were trained within the UK Biobank cohort precictionpredixtion of European ancestry and then validated in the predicfion independent study population ofindividuals The participants predicction the training and validation cohorts are independent from the predicyion analyzed predicion the previously conducted GWAS from predictiin summary predoction were obtained A total hj 51 candidate ancestry- and trait-specific scores were maryland live casino online in the genome-wide predkction score GPS training analysis, predictioh 32 scores carried forward on the basis of a stepwise process to identify those that significantly contributed to overall prediction and included in the weighting of GPS Mult Fig.

Polygenic scores were constructed using cohort-specific, ancestry-stratified summary statistics for CAD and CAD-related traits, resulting in 51 GPS across all traits and ancestries. For each trait for example, CAD the best-performing combination of cohort-specific, ancestry-stratified GPSs was determined using stepAIC, and their optimal mixing weights β were determined using logistic regression inindividuals of European ancestry in the UK Biobank training dataset.

The selected GPSs were linearly combined using these mixing weights to yield multi-ancestry scores predicting CAD for each trait layer 1. The best-performing combination of multi-ancestry, trait-specific GPSs was determined using stepAIC, and their optimal mixing weights β were determined using logistic regression inindividuals of European ancestry in the UK Biobank training dataset.

The selected GPSs were linearly combined using these mixing weights to yield GPS Mult layer 2. Ancestries: AFR, African; EA, East Asian; EUR, European; HISP, Hispanic; SA, South Asian.

Source GWAS traits: CAD 2733343856body mass index BMI 3857ischemic stroke 385859diabetes mellitus DM 596061peripheral artery disease PAD 385662glomerular filtration rate GFR 3863systolic blood pressure SBP 3864diastolic blood pressure DBP 3864LDL cholesterol 386566HDL cholesterol 386566triglycerides TG 3865 Of 51 ancestry- and trait-specific scores that were included in the GPS training analysis, 32 scores significantly contributed to overall prediction in GPS Mult after optimization of score selection with stepAIC and weighting through logistic regression in the two layers.

The resulting score, GPS Mult demonstrated a strong association with prevalent CAD, with significant improvement from previously published scores. This corresponded to a Nagelkerke R 2 of 0. GPS Mult showed stronger association with CAD risk when compared with the previously published GPS ref.

Among individuals of European ancestry, individuals in the bottom and top centile of the polygenic score had a 0. Among individuals with CAD, the median percentile of GPS Mult is significantly higher than that of the GPS75 interquartile range 50—91 versus 69 interquartile range 43—88 Fig.

Given improved stratification with this newly developed polygenic score, both tails of the score distribution were associated with a greater magnitude of risk when compared with GPS With the GPSthe top 8.

Conversely, with the GPSthe bottom 1. For all box plots: central line of each box, median; top and bottom edges of each box, first and third quartiles; whiskers extend 1.

eProportion of UK Biobank validation population with 3- 4- and 5-fold increased risk for CAD versus the middle quintile of the population, stratified by GPS.

The odds ratio assessed in a logistic regression model adjusted for age, sex, genotyping array and the first ten principal components of ancestry. Odds ratio assessed in a logistic regression model adjusted for age, sex, genotyping array and the first ten principal components of ancestry.

GPS Mult was also strongly associated with prevalent CAD in external cohorts, with significant improvement from prior published scores. For each group, individuals were selected for inclusion that were not included in any of the published GWAS summary statistics 3334 used for GPS Mult derivation.

Results for these and additional CAD polygenic scores published in the Polygenic Score Catalog are available in Supplementary Tables 6 and 7. The GPS Mult was predictive of incident CAD events over median interquartile range Across the entire UK Biobank validation study population without prior CAD, an incident CAD event was observed in 1.

Enhanced performance, indexed to the effect size observed in European ancestry with the GPSwas also observed across ancestries, with improved prediction in African ancestry relative effect size 0. GPS corresponds to a previously published polygenic score for CAD 9.

P values are derived from a Wald test implemented in the coxph function in R and are two-sided. bThe score effect sizes relative to the effect size of GPS in European ancestry individuals. GPS Mult designates polygenic score for CAD designed with summary statistics from multiple ancestries and multiple CAD-related traits in layer 2.

Asterisk designates the reference group for calculating relative gain. We additionally hypothesized that the GPS Mult could identify individuals in the extreme tails of its distribution with clinically important increase, or decrease, in risk.

In the high end of GPS Multwe sought to identify individuals with genetic risk of equivalent magnitude to that of individuals with these guideline-endorsed indications for statin therapy.

In prospective analyses of individuals without prior CAD, when compared with individuals in the middle quintile, those within the top 3 percentiles of GPS Mult had equivalent disease risk of incident CAD as the recurrent event risk for an individual who had a CAD event before enrollment Extended Data Fig.

Conversely, in the low end of the GPS Mult distribution, individuals in the bottom 5 percentiles were associated with a significant reduction in incident CAD risk HR 0. When comparing individuals who smoke and are in the bottom 5 percentiles of GPS Mult with nonsmokers in the middle quintile, the reduction in the absolute incidence of CAD associated with low GPS Mult offsets approximately 60 pack-years of smoking.

Furthermore, individuals in the 5th to 9th percentiles of GPS Mult also had a significant reduction in CAD risk HR 0.

These individuals experienced comparable risk reduction as those individuals carrying variants in PCSK9 -associated lifelong low levels of LDL cholesterol Extended Data Fig.

This combined model effectively improved risk prediction when compared with PCE alone. When binned into strata corresponding to clinical guideline recommendations 5this model suggested striking gradients in predicted CAD incidence across the GPS Mult distribution, with significant differences observed in ancestry-based subgroups Fig.

The absolute gradient in risk predicted by this model from bottom to top centile was largest in South Asian ancestry individuals with high PCE risk 5. bC-statistics are based on year follow-up events from Cox regression models of listed variables.

PCE includes age and sex variables in its risk estimation. cThe improvement in the predictive performance of the addition of the GPS Mult to the PCE was evaluated using continuous and categorized NRI, with a risk probability threshold of 7.

When compared with the PCE risk estimate incorporating clinical risk factors alone, integration of the PCE with GPS Mult contributed to significantly higher discrimination and predictive performance across the entire tested population. A gradient in improvement was seen using baseline models with age and sex alone C-statistic 0.

Similar improvements in C-statistic were observed for models tested in subgroups stratified by ancestry Supplementary Table 8. Second, categorized net reclassification improvement NRI was calculated across the entire study population using a threshold of 7. The risk model combining PCE and GPS Mult resulted in significant improvements in the categorical net reclassification index NRI 7.

Third, when compared with established risk-enhancing factors for CAD, categorization within the top 10 percentiles of the GPS Mult distribution corresponded to a significantly higher net reclassification over the use of PCE estimate alone 3.

Similar results in NRI were observed across other ancestries Supplementary Table 9. Additionally, similar trends in predictive performance, discrimination and reclassification were observed in a model that included integration of the QRISK clinical risk estimator, instead of the PCE, with GPS Mult Supplementary Tables 8 and 9.

In addition to first events, the GPS Mult predicted recurrent CAD events in individuals with prior CAD. Although a significantly less pronounced effect estimate as compared with the prediction of a first CAD event, the predictive performance of GPS Mult in this context was comparable to that of diastolic blood pressure HR 1.

A new polygenic score for CAD incorporating multi-ancestry summary statistics from GWAS for CAD and related risk factor traits on a large scale demonstrated significantly improved performance when compared to prior published scores.

The enhanced predictive capacity of this score was particularly pronounced in the extremes of the score distribution, enabling—in some cases—identification of healthy individuals with risk of CAD equivalent to those with pre-existing disease.

When added to risk scores used in current clinical practice, GPS Mult significantly improved discrimination and reclassification relevant to clinically important decision thresholds, such as the decision to initiate statin therapy.

This work builds on prior studies in providing a framework for optimizing a polygenic score for any trait, within the limitations of available GWAS with finite sample sizes and underrepresentation of diverse populations.

The GPS Mult incorporates CAD summary statistics from large non-European ancestry biobanks encompassing overcases and over 1, controls, including many-fold larger representation of individuals of non-European ancestries than previously published efforts 323338 The additional incorporation of genetic associations with CAD-related risk factors across ancestries into calculating GPS Mult significantly improves prediction beyond using summary statistics from CAD GWAS alone, with impact most notable in individuals of non-European ancestry.

This may potentially be due to greater representation of these ancestries in the discovery GWAS for CAD risk factor traits. With these additions, the phenotypic variance explained by GPS Mult for CAD calculated as R 2 on the logit-liability scale was 0.

Although this estimate remains below the estimated single nucleotide polymorphism heritability for CAD of 0. Overall, modest improvements in prediction were observed among individuals of African ancestry, in part due to underrepresentation of this group in GWASs so far, and these discrepancies warrant careful consideration as polygenic scores start to enter into clinical practice Due to smaller haplotype blocks observed in individuals of African ancestry, a 4- to 7-fold larger GWAS is needed to yield comparable prediction gains In the near term, the decreased effect size observed in individuals of African ancestry is likely to persist, and this has also been observed for other biomarkers and predictors in clinical practice Nevertheless, genetic ancestry has a considerable impact on certain aspects of polygenic risk prediction, such as the allele frequency of a given variant.

: X hh prediction

Test the HH-style channel

Necessarily Not Useful. Figure 1. Figure 2. Decision tree in UML activity diagram style depicting how each of four propositions about predictability at the individual cell and whole system levels can be determined.

Dashed arrows link the individual and whole system levels; diamonds represent decision points, with arcs labelled according to the conditions that must apply to follow them.

Thought experiment 1: Predicting cellular automata Table 2 provides a summary of the prediction problem outlined in this thought experiment. Multiplying the estimated number of atoms in the known universe by the Planck time since the big bang yields a smaller number. These numbers are nevertheless finite, and can be exhaustively explored in theory if not in practice.

Searching the space of transition functions exhaustively is therefore intractable in the general case, but not undecidable. Table 3: Instantiation of Table 1 for the CA thought experiment. Figure 3. Checking the predictability of an elementary CA.

The light blue cyan cells show the data, also using shade to represent a cell in state 1 or 0. In this particular run, all but four of the rules have been eliminated because they do not fit the data in the cyan cells. Figure 4. Heatmap showing the number of rules eliminated as max-data is increased from 1 to 40 with replications per setting.

Complication: Asynchrony Table 4 summarizes the prediction challenge caused by what might appear to be a trivial complication to the CA. Then, consider an instruction like the following: ask turtles [ forward [pcolor] of patch-here ask patch-here [ set pcolor [color] of myself ] ] This makes the colours of the patches and the distances moved by turtles other than the first asked sensitive to what previous turtles asked have done.

In real-world situations, people only act in synchronized ways by agreement, such as at traffic lights. Thought experiment 2: Asynchronous networks of Turing Machines Table 5 summarizes the prediction challenge in this thought experiment, which is based on several Turing Machines TMs operating asynchronously.

Table 6: Table showing the various predictability invariable and omissive and unpredictability asymmetric and symmetric conditions for the asynchronous network of Turing machines thought experiment. Figure 5. The problem of predicting in wicked systems with endogenous ontological novelty.

All states in the future system are then symmetrically unpredictable. Discussion In this article, we have defined four levels of predictability, and related them to usefulness with respect to possible system states predicted to occur or not to occur.

Figure 6. Summary of the expected predictability of various systems, using similar axes to Andersson et al. Borders show special cases: for CAs, when insufficient data are provided; for asynchronous TMs, when the set of schedules explored to make the prediction is a superset of the possible schedules; for wicked systems with endogenous ontological novelty, short-term predictions prior to transitioning.

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Ecological Complexity , 40 part B , Wonderful Life: The Burgess Shale and the Nature of History. London: Penguin. GRIMM, V. Pattern-oriented modeling of agent-based complex systems: Lessons from ecology.

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Ecology and Society , 11 2 , The 10 Vs, issues and challenges of big data. Win 7 Draw 2 Lose 1. Win 5 Draw 2 Lose 3. H2H Date Match League Result Game Odds Bookmaker 1 X 2 1xBet 1. Diriangén Last Results Date Match League Result More Predictions All Games.

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Football Tips Predictions Best football predictions website. Diriangén vs Real Estelí. All genes from the ISH data are shown. e Violin plot for the predictive accuracies of Perler for the LOOCV experiments for all genes in the reference ISH data.

f Prediction of non-landmark genes. In addition to the monotonic gene-expression profiles, non-monotonic gene-expression profiles are observed Hamp, Igfbp2, Cyp8b1, and Mup3 7. g , h Application of Perler for the mouse visual cortex.

g Cross-validation CV study tenfold. h ROC curve for the CV experiments of genes shown in g. i Violin plot representing the predictive accuracies of Perler for the tenfold CV experiments of all genes in the reference ISH dataset according to ROC score.

The median ROC score is 0. We then applied Perler to mammalian liver datasets Supplementary Figs. Finally, we applied Perler to adult mouse visual cortex datasets Supplementary Figs. CV revealed that Perler predicted the spatial expression patterns of genes according to both layer-specific expression and cell-type-specific expression in brain cortex Fig.

We also applied Perler to another scRNA-seq dataset , cells; Drop-viz 31 , and found that it predicted the spatial gene-expression patterns using the Drop-viz dataset consistently with the Allen Brain Atlas dataset Supplementary Figs.

These results suggest that Perler is applicable for prediction using high-dimensional spatial reference maps. Taken together, the findings support Perler as a powerful tool for predicting spatial gene-expression profiles in any multicellular system with general applicability to any type of ISH data e.

In this study, we developed a model-based computational method Perler that predicts genome-wide spatial transcriptomes. Perler sequentially conducted a two-step computation, with the first step mapping ISH data points to the scRNA-seq space according to the generative linear model by EM algorithm Fig.

Using a dataset for early Drosophila embryos, we demonstrated that Perler accurately reconstructed and predicted genome-wide spatial transcriptomes with robustness Figs.

Moreover, we showed that in any multicellular system, Perler displayed broad applicability to any type of ISH data Fig. We propose that Perler offers three innovative features. First, Perler can calibrate the difference between scRNA-seq and ISH measurement properties.

Second, Perler can reliably reconstruct gene-expression patterns in a noise-resistant manner. Specifically, Perler can evaluate to which extent each gene is reliable for reconstruction depending on the noise intensity Fig. As a result, more reliable genes with low noises have larger contribution to the weights for the reconstruction, whereas less reliable genes with high noises have smaller contribution.

It should be stressed that such quantitative evaluation of gene reliability is possible only with a method using a generative model. Third, the model-based linear mapping used in Perler is beneficial in terms of the performance for gene-expression pattern reconstruction. To ensure generalized performance, we introduced generative linear modeling with biologically interpretable constraints and statistically reasonable distances.

This model-based characteristic of Perler differs from Seurat v. Their model-free mapping addresses gene expression as continuous variables with applicability to any kind of multicellular system; however, these methods freely map ISH data to scRNA-seq data without any assumptions i.

Further, in a demonstration using Drosophila data, Perler was found to preserve the timing information of scRNA-seq data and robustly reconstruct the spatial gene-expression patterns of the pair-rule genes; whereas this kind of robustness is not observed in other model-free methods Liger, Seurat v.

For example, by focusing on the stripe-doubling of pair-rule genes in Drosophila , Perler successfully reconstructed stripe patterns at a single-cell resolution, while Seurat v. We believe that these results highlight the importance of using model-based prediction of spatial gene-expression patterns.

Additional characteristic features of Perler are summarized in Supplementary Table 5. It is worth mentioning a recent method called novoSpaRc This method proposed a new concept for predicting spatial expression patterns using the physical information of cells in tissue, which enables these predictions with little or no information regarding ISH gene-expression patterns.

However, in practice, their predictive ability using Drosophila scRNA-seq data is unsatisfactory at single-cell resolution; therefore, this concept of using cellular information remains challenging. As a focus of future study, it would be interesting to extend our generative model to introduce prior knowledge of physical information.

We demonstrated that Perler can integrate two distinct datasets of RNA-expression profiles, while also avoiding overfitting to the reference. These features suggest that Perler could be a suitable theoretical framework for integrating not only two RNA-expression datasets, but also two single-cell datasets with different modalities, such as chromatin accessibility measured by a single-cell assay for transposase-accessible chromatin, using sequencing and DNA methylation measured by chromatin immunoprecipitation sequencing.

Particularly in terms of multi-omics analysis, where datasets from two different modalities do not exactly match and are often sampled from different individuals and using different time intervals 33 , 34 , Perler can potentially help integrate different types of single-cell genomics data.

Thus, Perler provides a powerful and generalized framework for revealing the heterogeneity of multicellular systems. We developed a method to reconstruct spatial gene-expression profiles from an scRNA-seq dataset via comparison with a spatial reference map measured by ISH-based methods.

In the spatial reference map, landmark gene-expression vectors D genes; e. melanogaster embryos are available for all cells, whose locations in the tissue are known.

melanogaster embryos lack information regarding cell location in tissue. where y i and h i indicate expression levels of landmark gene i measured by scRNA-seq and ISH experiments, respectively; ξ i indicates Gaussian noise with zero mean and unit variance; and a i , b i , and c i are constant parameters for gene i , which are interpreted as scale difference amplification rates, background signals, and noise intensities, respectively.

We reduced the dimensionality of the genes to change Eq. In vector—matrix representation, the Eq. The dimensionalities of both scRNA-seq and reference data were reduced by PLSC analysis PLSC can extract the correlated coordinates from both datasets.

In PLSC analysis, the cross-correlation matrix of scRNA-seq and ISH data is first calculated as:. W is then subjected to singular value decomposition as:.

In this study, the metagene vectors for scRNA-seq x n and the reference data r k were, respectively, calculated by:. where u n and v k indicate the n th row vector of U and the k th row vector of V , respectively.

We used Eq. To infer from which cells in the tissue the scRNA-seq observations originated, we developed a generative model for metagene-expression vectors for scRNA-seq data x , which was expressed by a K -components GMM:. Note that A , b , and Σ are unknown parameters that need to be estimated.

To estimate the unknown parameters π , A , b , and Σ , we maximize the log likelihood function using the EM algorithm. In the E step, based on the current parameter values, we calculated the responsibility, which represents the posterior probability that scRNA-seq vector x n was derived from cell k in the tissue as:.

In the M step, we optimize the parameter values in order to maximize the log likelihood function based on the current responsibilities. These parameter values are updated as follows:.

The detailed derivation for these equations is presented in a later subsection. describing the mapped metagene-expression vector of cell k measured by ISH.

In Perler, updating π k is optional. Ideally, π k should be proportional to the number of cells within region k. Note, fixing π k accelerated the convergence of the EM algorithm compared with optimizing π k Supplementary Figs.

For the initialization of parameter values, we selected the values of a i and b i such that mean and variance of each element of x ni and r ki were the same and selected the c i values as standard deviation of x ni.

where y n indicates the n th scRNA-seq data point D- component vector. w nk is calculated by. where α , β , and δ are positive constants. Note that δ in the numerator and denominator of Eq. D nk indicates Mahalanobis distance between scRNA-seq data point x n and cell k :.

Note that Eq. Values of α and β are determined by CV. We calculated differentiation of weight with respect to each metagene-expression level of scRNA-seq data point as:. This relationship indicates that small changes in unreliable genes with high noise levels has little effect on the weight, while small changes in reliable genes with low levels of noise have a large effect on weight.

Therefore, Perler can reconstruct gene-expression profiles in a noise-resistant manner by accounting for the reliability of each gene through weight determination.

We optimized the hyperparameters α and β of the weighting function by LOOCV, in order to fit the predicted gene expression to the referenced gene expression measured by ISH.

To this end, we removed one of the landmark genes from the ISH data and used this dataset to predict the spatial gene-expression profile of the removed landmark gene with the fixed hyperparameters in Perler.

This LOO prediction was repeated for every landmark gene. We then quantitatively evaluated the predictive performance of these hyperparameters according to the mutual information existing between the predicted expression and referenced expression of all landmark genes:.

where J is the approximated mutual information between the predicted and referenced gene expression. The derivation of J is described in a later subsection. Here, we optimized α and β by grid search in order to maximize the mutual information, J.

We then used the optimized hyperparameters to predict the spatial profile of non-landmark genes Fig. To evaluate the predictive performance of Perler Fig. This re-optimization is repeated for every landmark gene.

Note that for the zebrafish embryo data, we used the ROC score instead of the correlation coefficient, because only the binary ISH data was available. In addition, for the mouse visual cortex data, we conducted tenfold CV because of the massive computational cost of LOOCV for the large number of landmark genes genes.

For D. In these datasets, the number of scRNA-seq data points is , whereas the number of cells to be estimated in the embryos is The expressed mRNA counts in this scRNA-seq dataset were already log normalized according to the total number of unique molecular identifiers for each cell.

For each gene, we subtracted the average expression from the scRNA-seq data. In addition, the ISH data were log-scaled and subtracted average expression from this ISH data, as same as the scRNA-seq data.

In these data, the number of scRNA-seq data points is , whereas the number of subregions to be estimated in the embryos is Note that the ISH data were binary. Similar to the Drosophila data, we log-scaled both scRNA-seq and ISH datasets and subtracted the average expression of each gene. For reconstruction of the mammalian liver, we used scRNA-seq and smFISH data provided by Halpern et al.

In these data, the number of scRNA-seq data points is , whereas the number of zones to be estimated in the embryos is 9. Because multiple samples were provided in the smFISH data, we calculated their average at each tissue location for Perler, followed by log-scaling both the scRNA-seq and smFISH data and subtracting the average expression of each gene.

For reconstruction of the mouse visual cortex, we used scRNA-seq data provided by the Allen Brain Institute 17 and Drop-seq data provided by Saunders et al. For ISH data, we used smFISH data provided by Wang et al.

The number of scRNA-seq data points is 14, and ,, respectively, whereas the number of cells to be estimated in the cortex is We log-scaled both the scRNA-seq and smFISH data, and subtracted the average expression of each gene. We used the newly developed method, Perler for data analysis.

Perler is built on Python 3. Because the embryo is bilaterally symmetric, we mapped the reconstructed spatial gene-expression levels of the cells in the right-half embryo. According to the previous study 5 , we then mirrored the spatial gene-expression levels of the right-half cells to the remaining cells in left-half embryo.

In the case of the mammalian liver, we visualized the reconstructed gene expression as a heatmap. In the case of the mouse visual cortex, we visualized the reconstructed gene expression at single-cell resolution.

We used two-dimensional coordinates of all cells within cortical slices provided by Wang et al. The generative model of scRNA-seq data point x with latent variables z is formulated, as follows.

The probability distribution of z is:. The probability distribution of x conditioned by z is:. The joint probability distribution of x and z is:. Note that the marginalized distribution of z becomes Eq. The likelihood function for the complete dataset { X , Z } is given as:.

Therefore, the expectation of its log likelihood function over the posterior distribution of P Z X , θ old becomes:. where γ nk is the expectation of z nk over P Z X, θ old given as:. In the E step, γ nk is calculated based on the current parameter values of θ old. In the M step, we update the parameter values θ by maximizing the Q -function as:.

We derived Eq. Here, we assumed that spatial expressions of landmark genes are independent from one another, which leads to:. Further information on research design is available in the Nature Research Reporting Summary linked to this article.

This study constituted a reanalysis of existing data. Source data are provided with this paper. Perler is developed under python 3. The minimal usage of Perler is provided in Supplementary Table 6 , and the selected parameters in the manuscript are provided in Supplementary Table 7.

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Detailed Trend Components of the Hooper Holmes Inc Forecast & Prognosis

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Gill , C. Gillis , A. Gilmore , M. Gilovitz , R. Gingold , D. In HH: Statistical Analysis and Data Display: Heiberger and Holland. plot R Documentation Plot confidence and prediction intervals for simple linear regression Description The data, the least squares line, the confidence interval lines, and the prediction interval lines for a simple linear regression lm y ~ x are displayed.

Usage ci. plot lm. S3 method for class 'lm' ci. name] , newdata, conf. frame lm. cex , Arguments lm.

object Linear model for one y and one x variable. xlim xlim for plot. newdata data. level Confidence level for intervals, defaults to. object newfit Constructed data. ylim ylim for plot.

Default is based on the constructed prediction interval. pch Plotting character for observed points. lty, lwd Line types and line width for fit and intervals. cex Font size for main title.

main Main title for plot Additional arguments to be passed to panel function. Value "trellis" object containing the plot. Note The predict. Author s Richard M. plot tmp. Related to ci. plot in HH HH index. R Package Documentation rdrr. io home R language documentation Run R code online.

Browse R Packages CRAN packages Bioconductor packages R-Forge packages GitHub packages. We want your feedback! Note that we can't provide technical support on individual packages.

You should contact the package authors for that. Tweet to rdrrHQ.

Account Options

Usage Impacts Details History Data Real-Time Solar Wind RTSW data refers to data from any spacecraft located upwind of Earth, typically orbiting the L1 Lagrange point, that is being tracked by the Real-Time Solar Wind Network of tracking stations. SWPC maintains the ability to instantaneously switch the spacecraft that provides the RTSW data.

Maybe the biggest one is, you can get access to all of the RTSW plasma and magnetometer since February As you zoom in to shorter time periods, the resolution of the data displayed will increase automatically.

The highest resolution available can be 1 second magnetometer and 20 second thermal plasma data. You can view data from the operational spacecraft or choose between DSCOVR and ACE.

The geomagnetic K and A indices can also be plotted. Tabs along the bottom of the plot allow different default plots to be chosen. These include data ranges of 2 hours up to ~20 years and displays with only Magnetometer, only Solar Wind Plasma, or a combination of both as well as other features described below.

Display of data values: When mousing over the data or 'tapping' with a mobile device , data values are displayed within the graph. Zoom: Zoom by click and holding on the start end of the interval and then moving the mouse to the end start of the interval before releasing the mouse click on mobile devices use 'pinch-zoom'.

This defines the zoom area. The thin bar at the bottom shows the zoom area relative to the full range of data. Double click tap zooms out to the default interval. Holding down the shift key while double-clicking steps out the time range by a factor of two.

Selects the display default duration. All plots automatically update adding new data on the right and dropping data on the left as it ages off. One can also choose to display data from DSCOVR, ACE, or the active spacecraft, where SWPC specifies which spacecraft is operational.

Recurrence data from 27 days 1 solar rotation earlier can be overplotted. The WSA-Enlil time series prediction can be displayed, as well as the Geospace propagated solar wind at 32 Earth radii.

Y-Axis scaling can also be set here. Finally, one can enter specific plot start and end times. They must be within the current default duration as specified by the left most button. Also, the brackets indicate that hh:mm:ss is optional. Do not include the brackets when entering a time.

txt containing the data shown in the current display. png of the current display. The data values displayed will default to the most recently available data.

The user can choose to connect the points with a 'Line', to just plot the points 'Marker', or use a hybrid approach. The eps format of melting curve is generated by Gnuplot. For the example shown in Fig. The predicted heat capacity melting curve shows two peaks around 60 and 90°C, respectively.

The peaks correspond to the melting of the two helices in the predicted structures in Fig. The input data of Vfold3D are the RNA sequence and the 2D structure base pairs see the snapshot of the Vfold3D web server in Fig.

The output of Vfold3D is a PDB file for the predicted all-atom 3D structure s. Because the current version of Vfold3D is template-based, no 3D structure will be predicted if a proper template cannot be found.

a For the most probable 2D structure shown in Fig. b For the predicted alternative structure shown in Fig. For example, as listed in Figure. Therefore, it is recommended to remove the single strand tails before submitting jobs to Vfold3D.

For the RNA in Fig. As shown in Fig. For the alternative 2D structure, which consists of two hairpins connected by a single-strand loop, Vfold3D yields no 3D structure because of the lack of the templates for the UUCG single-stranded open junction between the two hairpins.

Once a calculation is submitted, a notification page containing the job information job name, e-mail address optional and the job status is displayed. When the calculation is completed, the Vfold web server sends out an e-mail if provided notification with the predicted results attached.

It is recommended to bookmark the job-specific notification page for later check of the job status and for downloading Vfold predicted results, since Vfold2D and VfoldThermal might take a long computational time hours or even longer depending on the sequence length.

An online README file about the interpretation of the Vfold predictions is available on the Vfold web server. The Vfold package is developed to predict RNA structures and folding thermodynamics.

The web server will be updated continuously with the development of new Vfold-based algorithms for RNA folding. In the future development, we plan to add structure predictions for the formation of RNA-RNA complexes.

We will also add the effect of the ion-dependent electrostatic free energies and the heat capacity effect, which can cause the temperature-dependence of the enthalpy and entropy parameters for the loop and base stack formations, to the melting curve calculations and structure predictions.

Conceived and designed the experiments: SC XX PZ. Performed the experiments: XX PZ SC. Analyzed the data: XX PZ SC. Contributed to the writing of the manuscript: XX SC. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field.

Article Authors Metrics Comments Media Coverage Reader Comments Figures. Abstract Background The ever increasing discovery of non-coding RNAs leads to unprecedented demand for the accurate modeling of RNA folding, including the predictions of two-dimensional base pair and three-dimensional all-atom structures and folding stabilities.

Results The Vfold server offers a web interface to predict a RNA two-dimensional structure from the nucleotide sequence, b three-dimensional structure from the two-dimensional structure and the sequence, and c folding thermodynamics heat capacity melting curve from the sequence.

Conclusions The Vfold-based web server provides a user friendly tool for the prediction of RNA structure and stability. Introduction The increasing discoveries of noncoding RNAs demand more than ever the information about RNA structures [1] — [5].

Methods The Vfold model was first reported in for RNA secondary structure prediction [39]. Features of the Vfold algorithm One of the unique features of the Vfold model for 2D structure base pairs prediction is its ability to compute the RNA motif-based loop entropies.

Download: PPT. Results The Vfold server contains three parts: a Vfold2D predicts the RNA 2D structure pseudoknotted or non-pseudoknotted from the sequence, b VfoldThermal predicts the melting curve folding thermodynamics from the sequence, and c Vfold3D predicts RNA 3D structure for a given 2D structure and the sequence.

Vfold2D: Predicting RNA 2D structures from the sequence The input of Vfold2D is the sequence in plain text form see the snapshot of Vfold2D web server in Fig. Figure 2. An example of Vfold2D prediction: the input information highlighted in the snapshot of the Vfold2D web server are the sequence 32 nts in this example , the temperature 25°C , the energy parameters used for base stacks from MFOLD in this example the structural type non-pseudoknotted in this example.

VfoldThermal: predicting RNA melting curves VfoldThermal predicts the heat capacity C T melting curves from the temperature-dependence of the partition function Q T for the conformational ensemble chosen by the user.

Figure 3. An example of the VfoldThermal prediction: the inputs highlighted in the snapshot of VfoldThermal web server are the sequence 32 nts in this example with the temperature range of 0°C—°C, the energy parameters used for base stacks from MFOLD in this example and the structure type non-pseudoknotted in this example.

Vfold3D: Predicting RNA 3D structure The input data of Vfold3D are the RNA sequence and the 2D structure base pairs see the snapshot of the Vfold3D web server in Fig.

Figure 4. An example of the Vfold3D prediction: the snapshot of Vfold3D web server highlights the input sequence 32 nts for this example and the 2D structures as defined by the base pairs.

Vfold output Once a calculation is submitted, a notification page containing the job information job name, e-mail address optional and the job status is displayed.

Conclusion The Vfold package is developed to predict RNA structures and folding thermodynamics. Supporting Information. Data S1. s PDF. Acknowledgments We thank Dr. Song Cao for helpful discussions. Author Contributions Conceived and designed the experiments: SC XX PZ.

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and VA HSR RES 13— VA Informatics and Computing Infrastructure. The content of this manuscript does not represent the views of the Department of Veterans Affairs or the US Government. We thank Social Action for Health, Centre of The Cell, members of our Community Advisory Group, and staff who have recruited and collected data from volunteers.

Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA. Aniruddh P. Patel, Akl C. Fahed, Patrick T. Ellinor, Krishna G. Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.

Patel, Yunfeng Ruan, Satoshi Koyama, Saaket Agrawal, Akl C. Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA. Department of Medicine, Harvard Medical School, Boston, MA, USA.

Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA. CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China.

Veteran Affairs Boston Healthcare System, Boston, MA, USA. Stanford University School of Medicine, Palo Alto, CA, USA. Shoa L. Clarke, Philip S. Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA, USA. Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.

Veteran Affairs Atlanta Healthcare System, Decatur, GA, USA. Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK. British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, and Centre of Research Excellence, University of Cambridge, Cambridge, UK.

You can also search for this author in PubMed Google Scholar. Concept and design: A. Acquisition, analysis or interpretation of data: A.

Drafting of the manuscript: A. Critical revision of the manuscript for important intellectual content: P. and T. Correspondence to Minxian Wang or Amit V.

has served as a scientific advisor to Third Rock Ventures. is a co-founder of Goodpath and reports a grant from Abbott Vascular. receives sponsored research support from Bayer AG and IBM Research; he has also served on advisory boards or consulted for Bayer AG, MyoKardia and Novartis.

reports institutional grants from AstraZeneca, Bayer, Biogen, BioMarin, Bioverativ, Novartis, Regeneron and Sanofi. is an employee of Verve Therapeutics; has served as a scientific advisor to Amgen, Novartis, Silence Therapeutics, Korro Bio, Veritas International, Color Health, Third Rock Ventures, Illumina, Ambry and Foresite Labs; holds equity in Verve Therapeutics, Color Health and Foresite Labs; and is listed as a co-inventor on patent applications related to assessment and mitigation of risk associated with perturbations in body fat distribution.

The remaining authors declare no competing interests. Nature Medicine thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling editor: Michael Basson, in collaboration with the Nature Medicine team. GPS denotes previously published polygenic score for CAD 9.

GPS Mult designates polygenic score for CAD designed with summary statistics from multiple ancestries and multiple CAD-related traits. P values are derived from a t -test implemented in the GLM function in R and are two-sided. Proportion of UK Biobank validation population with 3, 4, and 5-fold increased risk for CAD versus the middle quintile of the population identified by GPS A and GPS Mult B.

The odds ratio assessed in a logistic regression model adjusted for age, sex, genotyping array, and the first ten principal components of ancestry.

Odds ratio assessed in a logistic regression model adjusted for age, sex, genotyping array, and the first ten principal components of ancestry. GPS: Genome-wide polygenic score; CAD: coronary artery disease. The estimated year CAD event risk was predicted using same model standardized to the mean of each of the covariates.

The estimated year CAD risk was predicted using same model standardized to the mean of each of the covariates. GPS: Genome-wide polygenic score. Net reclassification of coronary artery disease CAD cases and non-cases at the 7. BP: Blood pressure. BMI: Body-mass index. HgbA1c: Glycated hemoglobin.

LDL-C: Low-density lipoprotein cholesterol. HDL-C: High-density lipoprotein cholesterol. Open Access This article is licensed under a Creative Commons Attribution 4. Reprints and permissions. A multi-ancestry polygenic risk score improves risk prediction for coronary artery disease.

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Provided by the Springer Nature SharedIt content-sharing initiative. This affects prediction in that long-run dynamics of the system cannot trivially be identified analytically.

Beyond this restricted sense of complexity, Andersson et al. Andersson et al. As such, it is hard to see how exclusively narrative approaches, which necessarily lie in the realm of human cognition, can possibly do justice to the analysis of truly wicked systems. Simulation and prediction are useful even if the predictions of wicked problems are off the mark because of how they can bring in narrative approaches to be analysed beyond the means of human cognition.

That said, modelling used in isolation without a narrative or participatory approach has shown to lead to less useful outcomes in the context of wicked problems Davies et al. Equally, policy decision-making and strategy development for wicked problems have been shown to be insufficient without the help of simulation and prediction Loehman et al.

For example, with the proof of the four-colour conjecture Appel et al. The reasons why prediction in complex systems is said to be impossible, however, are more practical than they are theoretical.

In the rest of this paper, we develop definitions of various levels of predictability and relate them to the modal conceptualization of usefulness defined above. We develop arguments that complexity does not make prediction in empirical social-ecological systems undecidable because the search space of models is finite, and support these arguments with two thought experiments based on cellular automata and Turing machines.

The discussion considers the implications for using agent-based models in empirical wicked systems, with particular emphasis on the need to address endogenous novelty and the kinds of field research that can provide evidence to support that.

Importantly, it is then feasible to exhaustively search the space of transition rules of elementary CAs. Elementary CAs satisfy the conditions for complexity outlined in the introduction. The cells are the nodes in the intricate causal graph, and each cell has a direct causal connection only with its immediate two neighbours.

Wolfram a; has claimed Class 4 CAs are unpredictable, except by simulation. That is, if you want to know the future state of a Class 4 CA, then you need to know its current state and transition function, and then you can find only out what the future state is by running the CA.

Uniform CAs resolve into a state that repeats each time step, and hence do not satisfy the condition of having no absorbing state, whilst periodic CAs repeat a finite-length sequence of states, and are in that sense predictable in their long-run dynamics.

The second thought experiment is centred on Turing machines TMs. A TM is a computing engine comprising a tape of unbounded length, a read-write head, an internal state, and a state transition table.

The tape is divided into cells, each cell having one of a finite alphabet of possible values. The read-write head reads a symbol from the current cell on the tape, writes another symbol back to that cell, and then moves one cell left or right on the tape.

The state transition table of a TM specifies, for each combination of symbol read and internal state of the TM: the symbol to write to the tape, the next internal state of the TM, and whether to move the read-write head left or right on the tape. The CA thought experiment gives the simplest example of a system claimed to be complex, and demonstrates that prediction of such a system is nevertheless decidable, and in the very simplest case of elementary CAs, feasible.

The second thought experiment considers the case of a set of TMs all operating on the same tape, but executing their instructions in a non-computable order. TMs are universal computers. Anything that can be computed can be computed using a TM, including an ABM. If a TM can simulate an ABM, then a TM can simulate a single agent.

The thought experiments use their respective system specifications i. CA or asynchronous TMs to generate a dataset from a complex system, and then imagine that the means by which that dataset has been generated have been lost. They then try to regenerate and predict future states of the lost system by searching the space of all such systems, rejecting points in that search space if they do not match the data.

Besides considering the decidability of searching the space which essentially amounts to whether it is finite , we evaluate the usefulness of the possible resulting predictions with respect to four kinds of predictability, outlined in Table 1. Each of these predictabilities can be evaluated at the individual and whole-system level, making eight options in total.

Individual-level invariable predictability is possibly rather than necessarily useful because it would require a stakeholder to be concerned with the individual in question.

These predictabilities are based on the states predicted at the individual and whole system levels by models of the target system that match the data provided. System-level invariable predictability is obviously the ideal.

This can be achieved when there are enough data that there is only one matching deterministic model remaining from the set of models searched. Decreasing the size of the set of models searched using prior knowledge is another option. System-level omissive predictability is, as indicated, not necessarily useful.

For example, even if a large number of the possible system states are ruled out by predictions of the matching models, but all of those states are not significant to any stakeholder, the knowledge is unlikely to be useful. However, sometimes excluding one significant state from possible futures offers sufficient information, such as for extremely undesirable states.

The distribution of outcomes of asymmetric unpredictability is not a probability distribution in the case of deterministic models except in a Bayesian sense. Figure 2 shows how the various predictability conditions in Table 1 are related to each other.

It also highlights one argument for why prediction in complex systems should be possible: whole-system asymmetric and symmetric unpredictability should be ruled out by path dependence, which is stated explicitly by Thurner et al. In other words, if we find ourselves in a position where all possible states of a system are predicted by matching models to have non-zero probability, we might question whether that system is complex.

Rather more mundanely, system-level asymmetric and symmetric unpredictability require there to be at least as many matching deterministic models as there are states in the system.

In the nondeterministic model case, the number of states in the system needs to be smaller than the product of the number of models and the number of alternative options generated by the stochasticity in each.

Table 2 provides a summary of the prediction problem outlined in this thought experiment. Imagine that someone has built a Class 4 i. As Langton points out, the space of transition functions is huge.

It is reasonable to assume that having recorded enough time steps i. As the image was generated by simulation, we are not contradicting Wolfram by asserting that the image is our prediction for an image the forgotten transition function would have originally generated. Were the individual later to find the transition function they initially used, this prediction could even be verified.

Table 3 then provides a specification for the four predictabilities in Table 1 at the individual cell and whole image levels. Though searching the space of transition functions might require more matter and time than the universe provides, the complexity of CAs, which has been argued for frequently, is not a theoretical obstacle to prediction.

A minimum of two snapshots is needed to determine the transition rule using a method that relies on eliminating rules that do not reproduce later snapshots given earlier ones.

The first snapshot has many more cells in it than the second. The width of the second snapshot is controlled by a parameter max-data , which in the run in Figure 3 is In Figure 4 , the value of max-data is increased from 1 to 40, using replications of each setting 4, runs in total , to show the relationship it has with the number of rules eliminated n-eliminated.

Another point observable from Figure 3 is that the nature of the interdependencies of the cells means that, depending on the data provided and whether the CA is on an bounded grid, there is an inherent limit in how far ahead the prediction can be generated.

Table 4 summarizes the prediction challenge caused by what might appear to be a trivial complication to the CA. Asynchronous CAs have been studied for many years e.

Even so, there are special classes of asynchronous CA that have been proven capable of universal computation in a similar manner to that of the rule ECA Yamashita et al. It is not difficult to write an ABM that behaves in an analogous way.

Suppose NetLogo used a true random number generator 8 instead of the pseudo random number generator it uses in the ask command.

Then, consider an instruction like the following:. The scale of the computational problem posed by asynchrony for prediction should not be underestimated. For the purposes of illustration and comparison with the synchronous CA, imagine that we have somehow determined that rule was the only rule that matched some data from an asynchronous variant of an elementary CA, and we now wish to predict the future state of the CA.

This is exponential in both the number of cells 9 and the number of steps ahead to predict. Table 5 summarizes the prediction challenge in this thought experiment, which is based on several Turing Machines TMs operating asynchronously.

This combines the challenges outlined in Tables 2 and 4 with more sophisticated computing power at the micro-level. The interactions of the agents form networks. Networks of autonomous agents acting in a shared physical environment can be considered to be like a set of TMs all operating on the same tape.

If this order is random say, each of the multiple Turing Machines performs one step of computation each time a decay of a radioactive element is detected , the state of the tape at some point in the future is not computable in the general case. The portion of the tape from the left-most to the right-most non-null symbol is finite initially, and the machine can only move left or right one location in each step.

Further, when making predictions, not knowing the schedule means all possibilities have to be explored. If so, at least these positions will not be invariably predictable, and systemic invariable predictability will not apply.

For similar reasons, systemic symmetric predictability also seems unlikely, as from Figure 2 , systemic symmetric unpredictability is a subset of the cases where all individuals are symmetrically unpredictable.

If we maintain the argument that path-dependence means systemic asymmetric and symmetric unpredictabilities are inconsistent with the system under study being a complex system, and regard systemic invariable predictability as a negligible possibility for the reasons given above, omissive predictability is the only outcome at whole system level.

The difficulties with predicting states of a system are not only due to complexity. rational analytical, approaches fail to understand social-ecological systems in reality, for example, in urban planning.

Second, there is no definite solution to a wicked problem, since the solution may generate cascading waves of repercussions, e. Third, there is no clear set of potential solutions nor a well-described set of permissible operations. Fourth, related to the first criterion, the wicked problem can be explained in numerous ways.

Fifth, every wicked problem is unique. From these criteria, it follows that complexity is considered to be an integral part of wicked problems in the applied literature e. For Head , for example, wicked problems in real social-ecological systems, e. political systems, are determined by complexity subsystems and their interdependencies , uncertainty unknown consequences of action and changing patterns and divergence fragmentation in viewpoints.

Complexity is thus a necessary, but not sufficient, criterion for wickedness. From a systems perspective, Andersson et al. Wicked systems combine two system distinct qualities, complicatedness and complexity.

A complicated system is viewed to have a large number of components that behave in a well-understood way and have well-defined, but distinctive roles leading to the resulting effect.

Typical examples are usually machines such as helicopters or aeroplanes, which have millions of physical parts San Miguel et al.

The components of complicated systems are decomposable in that they can be analysed individually. Complex systems may also have a large number of components, but each component is of the same kind as in the cells of a CA , or at least, the number of classes of component is considerably smaller than in wicked systems.

The components of a complex system are thus not decomposable in the way they are in a complicated system, since the complex system would lose its emergent features and the single components their relatedness. Wicked systems combine both qualities, complexity and complicatedness.

The interactions among the components hamper analyses of the the rules of a single component. In the context of the TM thought experiment, representative ontological uncertainty pertains to such things as what the alphabet of the tape might be beyond those symbols already observable from the snapshots , or what states agents could have though really, it is only the maximum number of states they could have that matters.

If there are a finite number of alternative representations, then the problem of exploring the space of transition tables that match the data is the product of the problems of exploring each single representation. It is then that much more intractable, but still not undecidable.

In general beyond the immediate context of the TM thought experiment , empirical ontological uncertainty arises from disputation among domain experts in the wicked system, whose knowledge might be sought when attempting to model it. One way to address such uncertainty is to run the multiple alternative models with any of the multiple different initial conditions with which they are compatible.

Even so, if the number of alternatives to explore is finite, prediction based on exhaustive search of alternatives is ruled out on the grounds of tractability not decidability. However, if it was unlikely before that invariable system-level predictability would occur, it becomes vanishingly small as the number of alternative representations and initializations to explore increases.

On the other hand, the fact that the different representations will not lead to all symbols on the tape occurring in every matching run means that cell-level symmetric unpredictability becomes increasingly unlikely too, and with it, systemic asymmetric or symmetric unpredictability.

At the whole-system level, omissive predictability is then by far the most likely, if not the only, option. Computationally speaking, this is not the obstacle it might at first appear to be, as the new agents can initially run TM transition rules that effectively make them inactive until they are needed, and the novel states and symbols can be provided in extended sets, with special transition rules activated under whatever conditions it is that leads to the updated ontology applying.

These add exponentially to the intractability of modelling the system, but do not make the task undecidable. However, this misses the point. The real issue with wicked systems pertains to the data available when the prediction is needed.

However, other less extreme examples include scenarios exploring the introduction of new technology to a system, or new policies, and the ways in which agents might adapt and respond to them. Filatova et al. If the only data we have including description of the system itself when modelling pertains to the current system, we are obviously in a difficult position if we want to predict what a new system might look like.

In this article, we have defined four levels of predictability, and related them to usefulness with respect to possible system states predicted to occur or not to occur.

After some general considerations, we have then evaluated these predictabilities in complex and wicked systems. Figure 6 summarizes those findings. The network of asynchronous TMs thought experiment in Section 2.

Such asynchrony is not infeasible empirically, as in the real world agents act autonomously. This point challenges the idea that even a model that perfectly reproduces historical data can automatically be trusted to make point predictions in systems with analogous properties.

The implication of this for trusting predictions is that validity pertains to the representation of the system as well as numerical accuracy.

As made apparent by the application of the asynchronous TMs thought experiment to wicked systems in Section 2 , it is endogenous ontological uncertainty that poses the most significant challenge to prediction.

The problem, however, is not one of computability, but of data. In much the same way as the complete works of Shakespeare make no reference to smartphones, endogenous novelty means that presently available data provide less and less information about the future the further ahead predictions are required.

Generalizing this thinking from cellular automata shows that, when present states give you no information about relevant future states i.

Holtz et al. However, they also give a use case of giving advice to policymakers ibid. Our analysis suggests that trust based on fitting conditions in the current system cannot be generalized to trusting what the models say about the transitioned system.

Agent-based models of wicked systems need somehow to address novelty. The philosophy surrounding the role, building, verification, validation and use of simulation modelling in general rather than specific to ABM has been debated for over half a century.

The epistemological divides are often silo-related. An engineer using ABM has very few philosophical qualms about using it for prediction, but this paper addresses novelty in ABMs in the context of wicked problems — where many social scientists rather than engineers are building ABMs.

The importance of this debate for this paper, however, is that absolute in the sense of being objective, or universally agreed verification and validation of ABMs need not be achieved for prediction to be useful, even in the case of introducing novelty in systems. Use of cellular automata to model social systems has a long history, particularly in land use and urban systems Batty et al.

Spatial data from mapping, aerial photography and satellites offer rich, time-varying data that can be used for empirical applications of such models. The initial positions of the agents, and their specific internal states were not provided.

Knowing the number of internal states of the TMs is also unrealistic. These, as Hayek notes, are falsifiable theories, and in ecology, Grimm et al. The thought experiments were deliberately constructed in abstract rather than empirical systems so that their inherent complexity could be evaluated as a difficulty with prediction, and data availability and questions about representing the target system not be an obstacle or confounding factor.

The justification for more complicated agent-based models in empirical contexts has already been argued by Sun et al. There are several ways in which agent-based models can and do provide for novelty.

Polhill et al. These algorithms adjust the mechanisms by which agents make decisions based on their experiences operating in the model.

We are not aware of any models that provide such functionality, but note work such as that of Gessler in the Artificial Life community outlining such an agenda. Our observations about prediction in wicked systems are predicated on current data about the system becoming obsoleted by novel states in the future about which there is no information in the system as it is now.

Knowledge elicitation methods that anticipate what those states might be could be one way to obtain these kinds of data. García-Mira et al.

Narrative approaches can also be used as sources of qualitative data for models. Hassan et al. Critically, many of these guidelines entail documenting the social processes of building, using and evaluating the model. Documenting the social processes of model construction and use of data when making predictions records how a particular community addressed the wickedness of the system they were interested in, and makes explicit the intersubjectivity Cooper-White of any predictions.

Ahrweiler p. This process is not only the one incorporated in the simulation model itself. It is the whole interaction between stakeholders, study team, model, and findings.

Path dependency in complex systems mean they are predictable at the whole-system level in theory, if not in practice because of the intractability of exhaustively searching the space of models that might match the available data.

Wicked systems, however, pose a much more significant challenge to prediction, chiefly as a consequence of endogenous ontological novelty, rather than disputation about ontological structure.

Beyond the short term, the potential introduction of novel states in the system about which there are no data at the time the prediction is made means attempts at prediction are futile even if they were feasible. Though there are other purposes than prediction for building a model, there is still predictive utility to be had in modelling the current system and using this to detect transitions as the real world diverges from the states represented in the model.

Models of wicked systems, however, need to be more complicated than complex systems, and should include functionality that allows for novelty. Abstract This paper uses two thought experiments to argue that the complexity of the systems to which agent-based models ABMs are often applied is not the central source of difficulties ABMs have with prediction.

We define various levels of predictability, and argue that insofar as path-dependency is a necessary attribute of a complex system, ruling out states of the system means that there is at least the potential to say something useful.

Critically, however, neither complexity nor wickedness makes prediction theoretically impossible in the sense of being formally undecidable computationally-speaking: intractable being the more apt term given the exponential sizes of the spaces being searched.

However, endogenous ontological novelty in wicked systems is shown to render prediction futile beyond the immediately short term. To address this issue, the Critical Assessment of Protein Intrinsic Disorder CAID 2 was introduced to benchmark ID and binding predictors on a community-curated dataset of novel proteins obtained from the DisProt database 5.

In CAID, participants submit their implemented prediction software to the organizers, who generate predictions by executing the software on selected protein targets whose disorder annotations were not previously available.

Given a new protein sequence, the task of an IDR predictor is to assign a score to each residue for the tendency to be intrinsically disordered at any stage of the protein life. In CAID, both the accuracy of prediction methods and technical aspects related to software implementation are evaluated.

However, accessing the prediction power of the tools is not always possible. Often, the software is not publicly available, exists solely as a stand-alone executable, or is available as a web server with limitations. Moreover, publicly available methods are not standardized and require informed use, often entailing careful reading of the corresponding publication and interpreting predictors' output.

To address these issues, we present the CAID Prediction Portal, a web server that executes all CAID methods with a single click on a user-defined input sequence.

The server generates a standardized output and facilitates comparing methods, and it produces a consensus prediction that highlights high-confidence disordered regions. Disordered or binding residues are identified by selecting a threshold on the prediction score.

Depending on the type of benchmark, different thresholds can be selected, leading to different results. To guide the user in selecting the best parameters, the website is accompanied by extended documentation that explains the meaning of the different statistics presented in CAID and provides a brief description of all the methods.

While anonymous usage of the CAID Prediction Portal is always permitted, interested users can choose to use an optional log in to recover previous sessions via a private dashboard. An overview of the CAID Prediction Portal is provided in Figure 1. The CAID Prediction Portal needs to execute many different predictors on the same input sequence, provided by the user.

org that interacts with the scheduler controller of a computing cluster through the Distributed Resource Management Application API DRMAA 6 , a high-level API that provides a standardized interface for submitting and managing jobs on a wide range of cluster systems.

com as a job scheduler for the cluster. The purpose of this implementation is to allow users to submit, monitor and manage jobs on the computing cluster through a friendly web interface which exploits the RESTful API provided by the DRF. We also implemented various management features, such as the ability to stop or delete jobs, and to retrieve the job state, history and outputs for a particular user.

The server provides OAuth 2. When authenticated the user is able to recover previous sessions via a private dashboard. Non-authenticated users are allowed to create new jobs and access the results.

However, the amount of resources available to a single non-authenticated user is more limited, meaning that the number of daily and burst requests allowed is reduced. The DRF back-end is also responsible for managing all the possible jobs that can be submitted to the cluster, the resources to allocate for each specific job e.

CPUs, random access memory , and the dependencies that can be created between different jobs. For the CAID Prediction Portal, we created separate jobs for each of the available predictors, and a few additional jobs for creating input data for some predictors such as PSI-BLAST 7 , HHBlits 8 , SPIDER2 9.

This separation of predictors into different jobs is crucial as it provides flexibility to execute only the predictors of interest and display the results of fast predictors without waiting for others to finish. The CAID Prediction Portal includes a server dark background , which accepts a protein sequence as input, and a computing cluster pale background , which generates the output, which is available as a table TSV format and rendered in a dynamic feature viewer on the web interface.

io containers to containerize all the predictor software in order to standardize the input and output data, and ensure reproducible results.

By containerizing the software, we can ensure that the software runs consistently across different machines, and most importantly it is not needed to install it manually in each machine. Furthermore, containerizing the predictors enables us to package all the necessary software and dependencies together, making it easier to deploy and update the predictors.

With the creation of the container we also included scripts that are executed before and after the predictor, in order to standardize the input and output of the container, creating an interface with the predictor software.

The input of the predictor is a FASTA file containing multiple sequences, and the predictor is executed on each sequence, producing one output per sequence please note that this should not be confused with the input of the CAID server, which is restricted to a single sequence.

The execution time of the predictor for each sequence is also recorded. Some software present in the CAID Prediction Portal requires additional inputs, such as the results of PSI-BLAST, HHblits, or SPIDER2, to make their predictions. These additional inputs can be created inside the software's container itself, but they can also be provided in most of the cases as an additional parameter.

This ensures that the computation of common inputs is not duplicated, leading to faster and more efficient predictions. com containers because Singularity is designed specifically for high-performance computing environments and has several advantages in the context of computing clusters.

Firstly, Singularity does not require root access, making it easier to deploy and manage in a shared computing environment. Secondly, Singularity is optimized for running scientific workloads, with features such as support for MPI Message Passing Interface and GPUs Graphical Processing Units.

Thirdly, Singularity images can be easily hosted on a variety of storage systems, such as local filesystems, networked file systems, and cloud storage.

To make the container size smaller, some large datasets such as UniRef90 10 , Uniclust30 11 or large machine learning models are mounted inside the container at runtime. This approach allows the container to access these datasets only when needed, rather than including them in the container itself.

However, it is important to note that if these mounts are not created, the script that runs the predictor inside the container will fail with an error, since it will not be able to access the required data. In order to provide a comparison baseline, we also integrate the AlphaFold-disorder 12 method that infers disorder and binding predictions by exploiting AlphaFold predicted structures available in public databases As the last step of our standardization process, we opted to create individualized tasks for each predictor that can be conveniently executed through the CAID Prediction Portal.

This implementation grants users a heightened level of flexibility in their selection of methods, allowing them to make informed decisions that best suit their specific needs.

Each predictor execution is linked to an API call through the portal's front-end interface, while also remaining compatible with stand-alone usage for batch executions. The API is publicly available and lets third party services request specific predictions on demand.

Full documentation is available on the website. In Table 1 , we reported all methods available in the CAID server along with the corresponding publication when available.

These methods are a subset of those evaluated in the second round of the CAID challenge, i. those for which the authors gave permission or those that were already publicly available and licensed for free use. Given the repertoire of different flavors predicted by the various methods, in the CAID Prediction Portal, we divided them into two broad disorder and binding categories.

Users interested in specific subcategories or flavors are invited to read the description of the methods as reported on the website. All methods generate predictions from the protein sequence. Some methods require additional input which is generated by helper methods, e. BLAST or HHblits for sequence profiles.

In those cases, the additional input is generated once and shared with all dependent methods. The AlphaFold-disorder 12 method, instead of using the sequence, takes as input the protein structure predicted by AlphaFold.

In the CAID Prediction Portal the structure is retrieved directly from the AlphaFoldDB 13 database by searching the UniProtKB accession number.

The server tries to retrieve the accession number by querying the UniProtKB mapping service with the provided sequence encoded with the CRC64 algorithm, and selecting the first result. If the protein sequence is not present in the UniprotKB, no structure can be downloaded and the predictor will fail to execute.

Methods are listed in alphabetical order. The Type column indicates the type of output and the values in parentheses indicate the predictor name suffixes which correspond to different flavors or different implementations. When available, the corresponding publication is provided along with the corresponding authors.

For new methods, authors are those that submitted the method to CAID. The CAID Prediction Portal website allows users to execute the available predictors on a provided protein sequence. The server can process only one sequence at a time.

The predictors that are going to be executed can be configured, with some pre-made settings e. running only disorder, binding or quick predictors , or manually, selecting the predictors of interest. When submitting a new job, the user can also decide to associate a description to the job and an email address that will be used to send a notification when all the predictors will finish executing.

The job name is helpful to attach a text description or just a meaningful identifier to the input sequence, while the user email can be used to receive a notification when the calculation is done. After the submission, the user will be redirected to the results page. At the top of the page, a header card will be displayed, this contains various information about the execution status of the predictors, along with a control for stopping the jobs still executing, and a button to download all the currently available results in tab-separated values TSV format.

The result page will poll the back-end server to update the status of the jobs that did not finish yet, to retrieve their current status and download the results from the server when available. These results will be used to create and update a feature viewer, to display the outputs of the predictors.

These outputs are all aligned to the protein sequence that was submitted, and they can be of two different types, a binary score and a probability score. The feature viewer offers various controls to manipulate the display of the results. The predictions can be filtered based on their type disorder or binding , the threshold for the binary score can be changed from the predictor's default to optimized thresholds as provided by CAID.

Optimized thresholds correspond to a selection of metrics reported by the CAID challenge. The optimization strategy depends on the type of metric and validation dataset, those available in the CAID Prediction Portal are described in the website documentation, while we refer to the CAID paper 2 for a full description of all possible benchmarks.

The methods can be sorted based on their performance in CAID, disorder or binding content, or alphabetically based on their names. In the feature viewer, a consensus is also computed with the prediction of the available predictors, divided in the two categories, disordered and binding. This consensus is calculated as a majority vote of the binary predictions available.

The consensus will also be influenced by the chosen threshold. In order to compare predictions with structural and functional domains, Pfam 43 and Gene3D 44 assignments from the InterProScan 45 output are reported.

These annotations are calculated in parallel on a separate job, and shown as separate tracks on the feature viewer when available. While anonymous usage of the CAID Prediction Portal is always permitted, interested users can choose to recover previous sessions via a private dashboard after a login using their ORCID credentials, where all the previously submitted jobs can be accessed.

An anonymous user can recover a previous job by saving its UUID and later use it to access the results again. The CAID Prediction Portal is a valuable resource for researchers and scientists working in the field of protein structure and intrinsic disorder prediction. By combining state-of-the-art ID and binding prediction methods with the CAID optimization strategy, the portal allows users to calculate and compare different predictions in a single view.

Predictions can be dynamically adapted on the fly by choosing different CAID optimization strategies. For example, the user can focus on precision over recall, or on the contrary, can relax the optimization cutoffs to expand disorder detection.

One of the key advantages of the portal is its speed and dynamic nature, as the server displays the results of a method as soon as the calculation is completed. Additionally, the portal's modular and extensible design makes it easy to add or remove prediction methods at any time, providing maintainers with the flexibility to adapt to new developments in the field.

Finally, all methods are standardized and their output is made available in the same format. The CAID section of the portal provides benchmarking results and statistics that can guide users in the evaluation of the performance of the predictors.

This information is particularly useful for researchers who are looking to improve their methods and algorithms.

Moreover, the CAID Prediction Server is integrated into the OpenEBench 46 infrastructure for community benchmarking experiments of computational methods in the life sciences, which displays the results of various CAID editions in a dedicated section. This integration allows for the prediction output generated by the portal to be used in generating assessment results, thereby facilitating a transition from a timeframe-based challenge as was the case for CAID rounds 1 and 2 into a continuous assessment.

Last but not least, the CAID portal will help inform and improve the selection ID predictors available in the MobiDB database 47 for large-scale annotation of ID in proteins.

The latter is the main source of ID data for core data resources such as InterPro 48 and UniProtKB Any small improvement in ID prediction performance documented in the CAID Portal therefore has a large potential knock-on effect in improving ID annotations across the known protein universe.

In summary, the CAID Prediction Portal is a valuable resource that can help researchers develop more accurate and effective methods for predicting intrinsic protein disorder and their binding regions. By enabling continuous assessment and benchmarking of different prediction methods, the portal can help accelerate progress in this important field and benefit the scientific community at large.

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