dataesr/scientific-paragraphs-categorization huggingface dataset:
Datasets The predictive performances of MetAmyl were compared to existing methods using the three independent datasets called respectively training dataset, amylome subset and htt NT. The training dataset is a compilation of the Amylhex and Waltz databases designed by  [29]  and contains 278 hexapeptides for which experiments have been performed to determine their capacity to form amyloid aggregates. It is composed of 116 amyloid-forming hexapeptides (positive set) and 162 non-amyloidforming hexapeptides (negative set). In our study, the training dataset was first used to estimate MetAmyl score and next to evaluate the capacity of MetAmyl to predict the amyloid status of hexapeptides. The second dataset, called amylome subset, is composed of 33 proteins from the amylome and has been recently used to evaluate AMYLPRED2 predictions  [36].
The amylome subset is a collection of proteins for which experimental data validated 70 hot spots. Details regarding the dataset and the references that support hot spots validation can be found in Table  S1  of  [36]. Assessing the accuracy of hot spots prediction methods is a difficult task because there is only a relatively small number of experimentally confirmed amyloid regions (true positive, TP), and even less for confirmed non-amyloid regions (true negative, TN). Another essential element involved in the difficulty to classify peptides in a positive or negative group is linked to the polymorphism of amyloid fibrils, which may depend on the experimental conditions used  [45] [46] [47]. These are reasons why we chose the valuation dataset proposed by Tsolis et al. , enabling a fair comparison between AMYLPRED2 and MetAmyl which both propose consensus-based methods to predict aggregation prone regions.
In complement to the amylome subset we used a third dataset, called htt NT , recently published by Roland and collaborators  [48]. It is composed of peptides generated from the human Huntingtin protein. Using the 18 amino acids of the N terminus of the wild type protein the authors synthesized 15 scrambled sequences (peptides with the same amino acids but in a different order) whose aggregative properties were studied in vitro in simulated physiological conditions. As summarized in Table  S6 , 3 peptides grow rapidly into amyloid fibrils, 2 peptides aggregate more slowly and one aggregates only at high concentrations. It results that our third dataset is composed of 6 amyloid peptides and 10 non-amyloid peptides.
Comparative analysis Based on the training dataset, we first compared the overall performances of each predictor by the use of Receiver Operating Characteristic (ROC curves). ROC curves estimation is based on sensitivity and specificity that are estimated at different thresholds applied to the output score. Thus, ROC curve computation is not feasible for AMYLPRED2 as its prediction is binary. We further investigated the classification performances of each predictor based on confusion matrices obtained by applying provided hot spot thresholds to each score using the training dataset. , where TP (resp. TN, FP, and FN) is the number of True Positives (resp. True Negatives, False Positives and False Negatives). For the following individual predictors, 3D profile, AGGRESCAN, FoldAmyloid, PAFIG, PASTA, SALSA and TANGO, we used defined thresholds as proposed by the authors.
Comparative analysis on the training dataset The aim of our analysis of the training dataset was to evaluate and to compare the global accuracy of MetAmyl against existing predictors. At first, predictor performances were assessed by the use of ROC curves. Then, confusion matrices for each predictor were investigated in order to group predictive methods with respect to their statistical patterns. Figure  1  displays the ROC curves for MetAmyl, obtained with Leave-One-Out cross-validation, and the four selected predictors: FoldAmyloid, PAFIG, SALSA and Waltz. One can see that MetAmyl outperformed the individual predictors. MetAmyl ROC curve is indeed above the other curves except in case of very high sensitivity. Similar conclusions are obtained when comparing the ROC curves for all compared predictors (see Figure  S1 ).
I. ) that were obtained using bootstrap replicates  [43]. The comparison of MetAmyl AUC and the other methods is summarized by the p. value obtained with Delong's method  [49]. For the MetAmyl classifier, results were obtained using a Leave-One-Out Cross Validation. doi:10. 1371/journal. pone. 0079722. t001 specificities higher than 0:80. The counterpart is their poor sensitivity of their predictions. On the training dataset, our results demonstrate that using a weighted combination of predictors increases the prediction accuracy of hexapeptides. MetAmyl accuracy is not over estimated by effect of overfitting as results presented in Tables  1  and 2  used a Leave-One-Out cross-validation. Effect of cross-validation on MetAmyl performances is displayed in Table  S2  that reports the global measures (AUC, ACC and MCC) in four situations: no cross-validation, Leave-One-Out cross-validation, 10-fold crossvalidation and 20-fold cross-validation.
Numbers in brackets correspond to 95% confidence intervals (95% C. I. ) that were obtained using bootstrap replicates  (Robin et al. , 2011). For the MetAmyl classifier, results were obtained using a Leave-One-Out Cross Validation. doi:10. 1371/journal. pone. 0079722. t002 The study of the amylome subset shows that the four individual predictors including in MetAmyl, namely PAFIG, SALSA, Waltz and FolAmyloid, exhibit different statistical patterns. On one hand, PAFIG and Waltz are both specific with a reasonable sensitivity. On the other hand, SALSA is highly sensitive with a poor specificity and finally, FoldAmyloid has a very high specificity balanced by a very low sensitivity. Thus, MetAmyl performance on the amylome subset (independent of the training dataset) establishes that MetAmyl efficiently combines these four predictors according to their complementary statistical patterns.
Investigation of scrambled sequences from htt NT Using a recently published dataset composed of scrambled sequences from 17-amino acid peptide segment, we compared MetAmyl predictive performances with 9 existing predictors  [48]. Predictions are summarized by the three following statistical quantities: Q value, MCC and F1 score (see Table  4 ). Furthermore, 95% confidence intervals have been computed using bootstrap replicates (see Table  S7 ). Our comparative analysis shows that only 2 sequences over 16 are missclassified by MetAmyl which makes MetAmyl the best predictor with respect to Q value, MCC and F1 score (see Table  4 ). MetAmyl has indeed the best Q value (83. 33%) which is statistically higher than the other predictors as for example AMYLPRED2 that have the second highest Q value (75%).
Furthermore, MetAmyl has statistically the highest MCC (0. 74) while the second and third best MCC are AMYLPRED2 (0. 52) and Waltz (0. 48). Regarding F1 score, MetAmyl reach the score of 0. 8 and is followed by AMYLPRED2 (0. 7) that falls into MetAmyl 95% confidence interval. The 8 other predictors have a significantly lower F1 score. Our results show that the 4 individual predictors used to built MetAmyl score have complementary statistical patterns. On one hand PAFIG and FoldAmyloid are highly sensitive with a very poor specificity. On the other hand, Waltz combines high specificity and low sensitivity. Finally SALSA proposes an acceptable trade-off between specificity and sensitivity. Thus, MetAmyl performance on the Huntingtin dataset proves that MetAmyl efficiently combines these four predictors by using Waltz and SALSA specificities and PAFIG, SALSA and FoldAmyloid sensitivities.
Example of disease-associated variants In this section, we assess the capacity for MetAmyl to predict the effect of single mutations in human fibrinogen-a. Fibrinogen has multiple biological functions and is a key protein of the coagulation pathway. Mutations in this gene lead to several disorders including hereditary renal/cardiac amyloidosis. The amyloid fibrils found in patients with renal amyloidosis are composed of fragments of fibrinogen encompassing residues 500 to 580  [51, 52]. So, we performed a systematic literature analysis in order to identify all known mutations affecting this region of the protein and linked to renal amyloidosis  [53] [54] [55] [56] [57] [58] [59] [60] [61]. Our analysis also included screening of four databases: Ensembl  [62] , GEHT  [63] , cBioPortal  [64] , and www.
Discussion By using a weighted combination of selected individual scores, MetAmyl has efficiently integrated predictors into a metapredictor. We demonstrated in this paper the benefits of combining predictive algorithms based on a statistical framework. In the context of a complex trait, such as amyloidogenesis, merging existing predictors has allowed MetAmyl to account for a broad scale of features in a single predictor. The evaluation of MetAmyl on three independent datasets revealed its accuracy to predict amyloidogenic segments in polypeptide chains and/or proteins. On the training dataset, MetAmyl has a significantly higher AUC, Accuracy and Matthews correlation coefficient than the other predictors. Moreover, on the amylome dataset, MetAmyl has the best Q value, Matthews correlation coefficient and F1 score.
The potential overfitting for MetAmyl on the training dataset has been controlled by the use of cross-validation which is enhanced by MetAmyl performance on the amylome subset and the htt NT dataset. Although it is based on a large number of experimentally validated amyloid regions, the amylome subset suffers from a lack of validated non-amyloid regions, which can affect the results of performance calculations. For this reason, we used a third test set, named htt NT and independent of the training dataset the amylome subset. The htt NT dataset has been chosen as being unbiased with regard to the correct assignment of amyloid or non-amyloid. Furthermore, the htt NT dataset illustrates the fact that polypeptides whose order has been randomly shuffled, can lead to various aggregation patterns (see Table  S6 ).
In the result section above, we showed that MetAmyl gives the best classification accuracy in comparison to 9 other predictors. Also, MetAmyl outperforms Zyggregator, software tested by the authors of the htt NT dataset  [48, 65]. MetAmyl predicts a relevant hot spot of aggregation in four out of six amyloid-forming peptides (SP8, SP13, SP14, SP15). Moreover, none hot spot is predicted in the 10 non-amyloid peptides. The 100% specificity of MetAmyl on the htt NT dataset is explained by a contribution from Waltz. In addition, FoldAmyloid correctly predicts the 6 amyloid-forming peptides, which greatly contributes to increase lack of sensitivity of Waltz. Waltz uses a positionspecific scoring matrix for amyloid prediction, which, according to their authors, allows the distinction between amyloid fibrils and amorphous -sheet aggregates  [29].
Perhaps it is a reason why peptides SP10 and SP11 are not correctly predicted by MetAmyl. Indeed, electron micrographs of scrambled sequences shown in  [48]  are far from representing well-ordered fibrillar structures. MetAmyl behavior can be explained by the fact that it efficiently combines four complementary predictors: PAFIG, SALSA, Waltz and FoldAmyloid. First, our results demonstrated SALSA ability to detect amyloidogenic segments while being non-specific. SALSA has indeed a tendency to inappropriately predict hot spots: amyloidogenic prediction covers more than 56% of the studied subset of the amylome. Next, PAFIG and FoldAmyloid shared a very similar statistical pattern on both datasets. They are indeed sensitive (w0:8) but not specific (v0:6) on the training dataset. Furthermore, PAFIG and FoldAmyloid are specific on the amylome subset (w70%) but lack in sensitivity especially FoldAmyloid (v30%).
The clinical phenotype of the R554H variant is a thromboembolic pulmonary hypertension without renal amyloidosis (DATscore~0). Although prediction of a clinical phenotype is not yet possible by bioinformatics methods, the observations done on fibrinogen suggest that MetAmyl can help to predict the effects of mutations on aggregation propensity of proteins. It should also be noted that such analysis cannot be performed by a predictor without a score of prediction such as AMYLPRED2 for example. The use of a quantitative score for MetAmyl allows for investigating the effect of variants. Moreover, MetAmyl, available at http://metamyl. genouest. org/, allows for large-scale analysis of polypeptidic segments and/ or proteins. The screening, with MetAmyl, of dedicated amyloid protein databases might help to better understand the formation of amyloid fibrils  [67, 70].
Conclusion Accurate prediction of amyloid aggregation from the analysis of the primary sequence of proteins is a long and difficult way. In this paper we show that a statistical combination of several algorithms improves the reliability of individual methods. We sincerely thank all colleagues in the field who, in developing these methods have allowed the realization of an efficient meta-predictor. Generally, the predictors are tested on their ability to find amyloid-forming regions defined on the basis of in vitro experiments. However, it is known that amyloid fibers are polymorphic and their structural properties depend on many parameters  [71]. Therefore, accurate prediction of amyloid-forming regions needs a large increase in properly validated benchmark datasets, without noise regarding classification in amyloid fibrils and amorphous b-aggregates.
Acknowledgments We are very grateful to the authors of the software Waltz and Tango, and towards the VIB (www. vib. be) for providing us with the standalone versions of these programs. We thank  Antony Le Be  ´chec and  Erwan Rio  that helped us in the upstream steps of this project. We acknowledge  Christophe Hitte  and  Maud Marchal  for reading through the manuscript.
We recently developed a user-friendly web-based application called bc-GenExMiner (http://bcgenex. centregauducheau. fr), which offered the possibility to evaluate prognostic informativity of genes in breast cancer by means of a 'prognostic module'. In this study, we develop a new module called 'correlation module', which includes three kinds of gene expression correlation analyses. The first one computes correlation coefficient between 2 or more (up to 10) chosen genes. The second one produces two lists of genes that are most correlated (positively and negatively) to a 'tested' gene. A gene ontology (GO) mining function is also proposed to explore GO 'biological process', 'molecular function' and 'cellular component' terms enrichment for the output lists of most correlated genes. The third one explores gene expression correlation between the 15 telomeric and 15 centromeric genes surrounding a 'tested' gene.
Introduction The increasing amount of genomic data represents a new resource for fundamental and translational research, but it is limited in its use due to complexity and heterogeneity of the different studies; therefore, in its raw form, it still remains underexploited. To fully take benefit from this resource, bioinformatics processes, which preserve biological sense caught in annotated genomic data, have to be applied before developing automatized mining functionalities, e. g. biostatistics' analyses. We have recently developed a user-friendly web-based application called bc-GenExMiner, which offered the possibility to evaluate prognostic informativity of genes in breast cancer by means of a 'prognostic module' including three functionalities  (1). Statistical analyses were based on genomic data and corresponding bioclinical annotations of 21 studies.
System architecture and database content System implementation, data selection and data preprocessing are fully described elsewhere  (1). Briefly, bc-GenExMiner is powered by Apache with a MySQL relational database storage. Web interfaces are written in PHP v5 and JavaScript. Statistical analyses are performed with R software. Datasets included in the database were publicly available (Gene Expression Omnibus, ArrayExpress, Stanford microarray database and also on author's individual web pages). Non-Affymetrix platform data were ratio-normalized, and Affymetrix raw CEL data were MAS5-normalized. Data were then log2-transformed. Finally, to merge data of all studies and create pooled cohorts, data were converted to a common scale (median = 0 and standard deviation = 1).
Gene expression correlation analyses Gene correlation targeted analysis. In a gene correlation analysis, the user chooses 2 to 10 different genes, and all possible pairwise Pearson's correlation coefficients are computed, along with the associated P-value, on merged datasets based on seven different populations: all patients pooled together or subsets of patients by ER status (two subsets) and molecular subtype (four subsets). Molecular subtype annotation was performed by means of six molecular subtype predictors (MSPs) and lead to robust molecular subtype predictor classification (RMSPC), which only includes patients with concordant molecular subtype assignment for the 6 MSPs (1). Results are displayed in a correlation map, i. e. a table where each cell corresponds to a pairwise correlation and is coloured according to the correlation coefficient value, from dark blue (coefficient = À1) to dark red (coefficient = 1).
Correlation plots are also drawn to illustrate each pairwise correlation. Gene correlation exhaustive analysis. A gene correlation exhaustive analysis permits to know the genes that are best correlated, positively and negatively, to a given gene, chosen by the user: Pearson's correlation coefficient is computed between the chosen gene and all other genes that are present in bc-GenExMiner database. Analyses are computed on merged datasets and are based on five different sets of patients: all patients, RMSPC basal-like patients, RMSPC HER2+ patients, RMSPC luminal A patients and RMSPC luminal B patients. Genes with coefficient above 0. 40 in absolute value and with significant associated P-value (<0. 05) are retained. As a complement, GO terms directly linked to genes are screened to identify those underrepresented or overrepresented in the lists of genes that are most positively correlated to the chosen gene (including itself), most negatively correlated to the chosen gene or in the union of the two previous lists.
Gene ontology analysis. This functionality is based on the mining of the three GO trees (biological process, molecular function and cellular component)  (2). A GO analysis finds GO terms that are significantly linked to a given list of genes ('target list'). For each term of each of the GO trees, comparison is done between the number of occurrences of this term in the 'target list', i. e. the number of times this term is directly linked to a gene, and the number of occurrences of this term in the 'gene universe' (all of the genes that are expressed in the database) by means of Fisher's exact test (P < 0. 01 is considered significant for this analysis). GO terms database was downloaded from geneontology. org and will be updated every 6 months as gene annotations. Gene correlation analysis by chromosomal location.
DNA location data were computed with infor- mation extracted from The Ensembl Genome Database (http://www. ensembl. org), NCBI Map Viewer (http://www. ncbi. nlm. nih. gov/mapview/) and NCBI Unigene (http:// www. ncbi. nlm. nih. gov/sites/entrez?db=unigene) websites. Only gene position (start and end) and DNA strand (positive or negative) were kept in our final list of chromosomal locations. In a gene correlation analysis by chromosomal location, the user chooses one gene, and Pearson's correlation coefficients, with associated P-values, are computed on merged datasets between this gene and genes continuously located around it on the same chromosome (15 centromeric genes and 15 telomeric genes), based on all patients or on different subsets depending on the ER status or on the molecular subtype.
Gene expression TCA permits to explore link between pairs of genes in an intuitive manner. Therefore, the user can easily and rapidly test its intuition or benchmark results. Gene correlation exhaustive analysis extracts genes that are correlated with the gene of interest in a non-intuitive, i. e. screening, manner. Computation involves all genes included in bc-GenExMiner genomic database (n = 20306). Non-intuitive and automatized interpretation by means of GO terms enrichment is proposed to help researchers to give a biological sense to these data. Gene correlation analysis by chromosomal location has been developed to identify DNA continuous clusters of correlated co-expressed genes in a cohort composed of all patients or on different molecular subtype breast cancer patients, and so to evaluate whether these clusters are specific of molecular subtypes.
ß The Author(s) 2013. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons. org/licenses/by-nc/3. 0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. page number not for citation purposes) Database, Vol. 2013, Article ID bas060, doi:10. 1093/database/bas060.
Page 1 of 9 ( 9. ß The Author(s) 2013. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons. org/licenses/by-nc/3. 0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. page number not for citation purposes) Database, Vol. 2013, Article ID bas060, doi:10. 1093/database/bas060.
For these Affymetrix Õ cohorts, ER status was determined based on 205225_at Affymetrix Õ probes (U133 array; cohorts GSE1456 and GSE11121) or on the median values of Affymetrix Õ probes representing ESR1 (U95 array; cohort GSE3143) using a two-component Gaussian mixture distribution model (3).
We applied two simplified models that reduce the computational cost of simulation: a 4D model that treats the tumor as an isotropic sphere (referred to here as the "4D-spherical" model), and a second, purely linear model of growth (the "Linear" model). The 4D-spherical model is simpler and computationally faster than the 4D-anatomical model, but still requires software that can solve partial differential equations, such as MATLAB® (The MathWorks, Natick, MA). Generally, Days Gained scores from the 4Dspherical model can be computed for one patient on the order of seconds to minutes on the 3. 5 GHz desktop computer used for this study. Scores from the simpler Linear model can be computed almost instantaneously using one algebraic equation (see Materials and Methods). In theory, they can be generated using any software that processes algebraic relationships, or even from paper-based nomograms or look-up tables.
Using this relationship and Fisher's equation for traveling wave velocity ( ) we identify D and ρ separately. We then simulate the growth of the tumor to the post-treatment time point, accounting for its unique diffusive and proliferative dynamics and the complex architecture of the brain with the following reactiondiffusion partial differential equation: ( ) 2 The equation describes the density of glioma cancer cells c in terms of D, ρ and the tumor cell carrying capacity of the tissue k (10 8 cells/cm 3 ) (  16 ). In our 4D-anatomical simulations we simulate tumor growth within a spatial discretization that is based on the patient's day-of-operation, neuro-navigational, high resolution T1Gd MRI. We use the Statistical Parametric Mapping  (22)  toolbox in MATLAB to automatically segment the gray matter, white matter and cerebro-spinal fluid (CSF) regions of the brain.