dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Integration Prior to the development of high throughput methods for extensive metabolome, proteome, transcriptome, genome and recently phenome research, scientists dreamed about the integration of different datasets to gain a deeper insight. Data integration, as a tool to enable in-depth insights into processes, can be performed at two levels. On the one hand, one can integrate homogeneous data derived from measurements on the same entity made in the same experimental set-up. On the other hand, meta-analysis can be applied to integrate heterogeneous data derived from different experiments  (Dupae et al. , 2014). The latter approach poses a challenge as the integration of data performed using different standards and methods are difficult. Thus, we will focus on the integration of homogenous data derived from the same experimental set-up that has been used to get a clearer view on plant (dys)function.
Over the past few years, questions on how to unravel the existing links between the metabolome, proteome, transcriptome, genome and the phenome from a particular entity have been discussed. As well as how this knowledge can provide better insights in mechanisms like abiotic stress tolerance or biotic stress resistance. Statistical multivariate methods have been refined to uncover those links and are now used to integrate two or even more datasets based on predefined models of one-by-one dataset relationships  (Le Cao et al. , 2011; Gunther et al. , 2014; Tenenhaus et al. , 2014). This not only makes it possible to integrate multiple 'omic levels, but even enables scientists to "supervise" and direct such analyses toward meaningful relationships between the 'omics datasets  (Gunther et al. , 2014).
A multivariate analysis approach becomes of interest because the algorithms are designed to explain as much of the variability at the plant level as well as the variability at the cellular level. This method is especially useful to assess polygenic traits such as drought tolerance, because one can specifically look for cellular and plant level variations that explain the variation in genotypes and treatments used. In this way candidate genes related to important tolerance mechanisms at the plant level can be retained for further analysis, a procedure called feature selection. Retained candidate genes can further be related to the kyoto encyclopedia of genes and genomes (KEGG), gene ontology (GO), protein domain, protein family, protein function, and more categorical databases, as a form of heterogeneous data integration  (Gomez-Cabrero et al.
Generation of algebraic data To enable the computational analysis of a chemical network the reactions should be presented in a format that enables the accurate representation of the network and allows the computational extraction of required data. For our computations we use the XML-based format SBML  [23] , which is widely used in biological research. For our computations we parse the SBML file that presents the chemical network and generate the necessary algebraic data using our PoCaB platform  [24]. PoCaB is a software infrastructure and data base that is used to explore algebraic methods for bio-chemical reaction networks. It provides tools to extract relevant algebraic entities from the network description such as stoichiometric matrices and their factorizations, kinetic matrices, polynomial systems, deficiencies and differential equations.
Computational logic tools In order to decide or to reduce the generated first-order formulae describing the existence of Hopf bifurcation fixed points we can use tools from computational logic. The ones used for the examples described below are the following: qepcad  [25]  implements partial cylindrical algebraic decomposition (CAD). The development of qepcad started with the early work of Collins and his collaborators on CAD circa 1973 and continues to this today. qepcad is supplemented by another software package called SLFQ for simplifying quantifier-free formulas using CAD. Both qepcad and SLFQ are freely available. 4 he SLFQ system 5  uses qepcad as a black box for simplifying quantifier-free formulas. qepcad is able to simplify formulae, but its time and space requirements become prohibitive when input formulae are large.
Example 8: control of DNA replication in fission yeast As another high-dimensional example, we consider the 22 dimensional model that describes the control of DNA replication in fission yeast. It is described in  [53]  and stored as a curated model in the BioModels database  [57]  with the ID BIOMOD0007. The stoichiometric matrix, the kinetic matrix, the set of extreme currents, and other algebraic data for this example can be obtained from the database PoCaB ("platform to explore algebraic methods for bio-chemical reaction networks"). 14 The flux cone of this model is spanned by 22 extreme currents and yields 230 2-faces and 1539 3-faces. Using the HoCaT method we were able to detect the existence of Hopf bifurcations in 69 cases of the 3-faces and its absence in the 2-faces. The computation of this example also demonstrates the efficiency of our method, as it enables even the analysis of a 22-dimensional system.
Acknowledgements This research was supported in part by  Deutsche Forschungsgemeinschaft  grants  Se 770/8-1  and We 1945/6-1 and by the  German Transregional Collaborative Research Center SFB/TR   14  AVACS. Thomas Sturm would like to thank  B. Barber  for his support with QuickHull and convex hull computation and  W. Hagemann  and  M. Košta  for helpful discussions on aspects of linear programming. We are grateful to the anonymous reviewers, whose insightful comments helped to improve the paper.
bAckground: DNA microarray studies identified gene expression signatures predictive of metastatic relapse in early breast cancer. Standard feature selection procedures applied to reduce the set of predictive genes did not take into account the correlation between genes. In this paper, we studied the performances of three high-dimensional regression methods -CoxBoost, LASSO (Least Absolute Shrinkage and Selection Operator), and Elastic net -to identify prognostic signatures in patients with early breast cancer. Methods: We analyzed three public retrospective datasets, including a total of 384 patients with axillary lymph node-negative breast cancer. The Amsterdam van't Veer's training set of 78 patients was used to determine the optimal gene sets and classifiers using sensitivity thresholds resulting in misclassification of no more than 10% of the poor-prognosis group.
To ensure the comparability between different methods, an automatic selection procedure was used to determine the number of genes included in each model. The van de Vijver's and Desmedt's datasets were used as validation sets to evaluate separately the prognostic performances of our classifiers. The results were compared to the original Amsterdam 70-gene classifier. results: The automatic selection procedure reduced the number of predictive genes up to a minimum of six genes. In the two validation sets, the three models (Elastic net, LASSO, and CoxBoost) led to the definition of genomic classifiers predicting the 5-year metastatic status with similar performances, with respective 59, 56, and 54% accuracy, 83, 75, and 83% sensitivity, and 53, 52, and 48% specificity in the Desmedt's dataset.
7 As opposed to standard multivariable regression analysis, these selection methods handle high-dimensional regression variables with no prior feature selection step by shrinking all the regression coefficients toward zero, and thus forcing many regression variables to be exactly zero. ] [10] [11] [12]  To our knowledge, the accuracy and predictive performances in independent validation sets of classifiers derived from high-dimensional Cox regression models have been less studied. In this article, we have used a systematic approach for training an entire classifier based on three such modeling strategies on the well-known original van't Veer dataset. The selection of predictive genes and the rules associated with the prognostic classifier were developed in the training set selected by van't Veer et al, thus allowing a direct comparison with the well-established Amsterdam 70-gene signature yet used in clinical routine in several countries.
Materials and Methods breast cancer datasets. We downloaded three retrospective datasets of node-negative early breast cancer samples profiled using oligonucleotide microarrays (Supplementary Table  1 ). Gene expression and histoclinical data were retrieved from National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO) database and authors' websites. Only samples from women with all the following annotations available were selected for the present study: patients' age, pathological tumor size and grade, ER and V-ERB-B2 avian erythroblastic leukemia viral oncogene homolog 2 (ERBB2) statutes, and follow-up. The van't Veer's dataset included 97 patients (78 in the training set and 19 in the validation set),  1  46 of whom developed distant metastasis within 5 years. The van de Vijver's dataset included 151 patients (61 of whom were also in the van't Veer's study), 2 40 of whom developed distant metastasis within 5 years.
The Desmedt's dataset included 197 patients,  13  of whom 36 developed distant metastasis within 5 years. The three datasets included a total of 384 unique pretreatment samples, whose histoclinical characteristics are summarized in Table  1. gene expression data processing. The three datasets used two different oligonucleotide-based technological platforms (Agilent and Affymetrix). Before analysis, data were processed. First, we mapped hybridization probes. Agilent probes annotations were retrieved and updated using both SOURCE (http://smd. stanford. edu/cgi-bin/source/source-Search) and EntrezGene (Homo sapiens gene information db, release from 09/12/2008, ftp://ftp. ncbi. nlm. nih. gov/ gene/), whereas Affymetrix annotations were updated using NetAffx Annotation files (www. affymetrix. com; release from 01/12/2008).
All probes were then mapped based on their EntrezGeneID. When multiple probes represented the same GeneID, the one with the highest variance was selected to represent the GeneID in the corresponding dataset. Second, each dataset was normalized separately. For the two Agilent sets, we applied quantile normalization to available processed data. For the Affymetrix set, we applied robust multichip average (RMA) to the raw data with the non-parametric quantile algorithm as normalization parameter. 14 Quantile normalization and RMA were done in R using Bioconductor and associated packages. As the dataset used for training the models were the van't Veer's one, we also proceeded with these data, as the authors, to a final selection of the probes significantly regulated, namely, with at least a two-fold difference in more than five tumors.
Choice of tuning parameters. To ensure the comparability between different methods, we used an automatic selection procedure to set the parameters and to determine the number of predictive genes to be included in each model. 8 The gene expression levels were treated as continuous values in these models. For the CoxBoost model, we used the cross-validation routine optimCoxBoostPenalty implemented in the package CoxBoost for R version 1. 4  16  to first determine the optimal penalty (amount of shrinkage), with a k-fold equal to 5. Once this parameter was determined, the other tuning parameter of the algorithm, namely, the number of boosting steps to perform, was selected via the function cv. CoxBoost, with also a k-fold equal to 5. The dimension of the selected multivariate Cox model was finally set by the principal routine CoxBoost.
For the LASSO and Elastic net methods, the regularization parameter λ was chosen via the cross-validation routine cv. glmnet before running the main algorithm implemented in the R package glmnet version 1. 9-8,  17, 18  with a k-fold equal to 5. The other tuning parameter of the Elastic net method, the mixing parameter α, which determines the balance between the L1 and L2 penalties, was set to a default value of 0. 5 in order to have a more flexible alternative to the LASSO procedure. Prognosis prediction. For each selected multivariate model, a classifier predicting the risk of metastatic relapse was based on the linear predictor given by the model. Following van't Veer et al,  1  the threshold for discriminating between predicted poor prognosis vs. good prognosis was determined as the sensitivity threshold (defined as the proportion of actual poorprognosis patients assigned to the predicted poor-prognosis class) resulting in misclassification of no more than 10% of the poor-prognosis group in the training set.
In the first step, each method was applied and the models were trained in the original van't Veer's training set of 78 patients, 1 34 of whom showed metastatic relapse within 5 years. In the second phase, the methodology was repeated in the same training set of 78 patients, but applied only to the 10,002 genes shared by the van't Veer's, van de Vijver's, and Desmedt's genomics platforms to make possible the application of defined multigene predictors in their entirety on the two latter datasets for testing their robustness. strategy for assessing and comparing performances of the prognostic classifiers. The clinical endpoint was the MFS, defined as the duration from the date of diagnosis of breast cancer to the date of metastatic relapse and censored after 5 years of follow-up. The predictive ability and the robustness of the classifiers were first assessed on the original van't Veer's validation set (19 patients).
We will refer to this validation as internal validation. In the second step, they were evaluated in the independent van de Vijver's and Desmedt's datasets. 2, 13 We will refer to this validation step as external validation. The sample classifications based on the 70-gene signature were retrieved for the three datasets in their respective original publications and served as a benchmark classification of tumor samples. Prognostic evaluation criteria. The main evaluation criteria of each classifier, including the 70-gene signature, in each validation set were its sensitivity, specificity, and accuracy in term of prediction of metastatic relapse at 5 years. The sensitivity was defined as the proportion of actual relapses within 5 years attributed to the poor-prognosis class. The specificity was defined as the proportion of patients remaining relapsefree at 5 years attributed to the good-prognosis class.
We also assessed the concordance of classifications based on the four models by using two-way contingency-table analyses and Fisher's exact test. Biological coherence. To explore the biological pathways linked to our classifiers in breast cancer, we applied Gene Set Enrichment Analysis (GSEA) (http://www. broadinstitute. org/gsea/) for comparing the expression profiles of predicted poor-prognosis vs. good-prognosis classes in the van de Vijver's and Desmedt's datasets. 19 Analysis was limited to the gene ontology (GO) gene sets (list C5) from the Molecular Signatures database (http://www. broadinstitute. org/gsea/ msigdb). Before analysis, each dataset was filtered to remove probes with low and poorly measured expression and standard deviation inferior to 0. 25 log 2 units, resulting in 11,184 and 12,712 genes, respectively.
results Internal performances and validation of the prognostic classifiers. CoxBoost, LASSO, and Elastic net applied to the van't Veer's training set (78 samples) identified prognostic models of 19, 23, and 36 genes, respectively, whose lists are nested, except for two genes of the CoxBoost model, which are not included in the two latter lists (data not shown). Each model was then applied to classify these 78 samples. As expected, the performances were excellent (Table  2 ): accuracy, sensitivity, and specificity of classifications were, respectively, 94, 91, and 95% for the CoxBoost model, 95, 91, and 98% for the LASSO model, and 95, 91, and 98% for the Elastic net model. For comparison, the Amsterdam 70-gene signature showed 78% accuracy, 91% sensitivity, and 68% specificity. The predictive performance of our models was then tested in the internal validation set (n = 19 patients).
93 [1. 89-42. 21] (P = 0. 001) for the Amsterdam 70-gene signature. external validation and performances of the prognostic classifiers. The same methodology was repeated in the same training set (78 samples), but limited to the 10,002 genes common to the genomics platforms used in the van't Veer's, van de Vijver's, and Desmedt's datasets, to define genomic predictors applicable in their entirety on the two external validation datasets. The optimal number of genes was smaller than previously, with 6, 18, and 29 genes for CoxBoost, LASSO, and Elastic net, respectively (Supplementary Table  2 ), but showed clear overlap with the corresponding gene lists above identified in the van't Veer's learning set (data not shown). These 6-, 18-, and 29-gene classifiers also showed strong overlap when compared together: the six genes included in the CoxBoost model were included in the 18 genes of the LASSO model, which were all but one included in the 29-gene Elastic net model.
Each classifier was then applied to the two external validation sets separately, and its performances were tested. van de Vijver's dataset. Results of sensitivity, specificity, and accuracy in the van de Vijver's population are shown in Table  3. CoxBoost, LASSO, and Elastic net kept good and comparable performances, while reaching until 62% in specificity for Elastic net. The CoxBoost, LASSO, and Elastic net classifiers were quite accurate (63, 64, and 66%, respectively). Particularly, the Elastic net signature was stable in terms of sensitivity. These results obtained in a larger independent validation set confirmed the robustness of our classifiers and suggested similar performances between them. HRs for metastatic relapse in the predicted poor-prognosis class as compared to the predicted good-prognosis class were 4.
The CoxBoost, LASSO, and Elastic net classifiers were also highly significant in multivariate models, with HRs above 3. 61 (Table  4 ). Particularly, they were more significant while having HRs inferior to that of the Amsterdam 70-gene signature. The low specificity of the Amsterdam signature may explain its high and more variable HRs. Desmedt's dataset. In the Desmedt's set (Table  3 ), the CoxBoost, LASSO, and Elastic net classifiers had again a good sensitivity (83, 75, and 83%, respectively), while having a specificity above 50% for LASSO and Elastic net (52 and 53%, respectively). The latter classifier remained the most accurate (59%), outperforming by 14% the very sensitive (97%) Amsterdam 70-gene signature that was 45% accurate. The univariate HRs for metastatic relapse were 4. 14 [1.
72-9. 95] (P , 0. 001) for the CoxBoost classifier, 2. 96 [1. 39-6. 3] (P = 0. 003) for the LASSO classifier, 4. 95 [2. 06-11. 89] (P , 0. 001) for the Elastic net classifier, and 15. 19 [2. 08-110. 88] (P , 0. 001) for the Amsterdam 70-gene signature. The related MFS curves (Fig. 2 ) had a similar pattern to their van de Vijver's dataset counterparts, with some patients misclassified close to the 5-year timepoint. The Elastic net classifier was again more significant in the multivariate model (Table  4 ) than the Amsterdam 70-gene classifier (P = 0. 003 and P = 0. 005, respectively), while having HRs inferior but less variable. Especially, the other two classifiers (CoxBoost and LASSO) were also confirmed in a multivariate model on this external validation dataset. concordance of predictions and biological significance of the classifiers.
We compared the concordances of predictions (prognostic classifications) based on the three models and the 70-gene signature using two-way contingencytable analyses in each external validation set. All the comparisons yielded significant correlations (P , 0. 001, Fisher's exact test). The concordance rates (Supplementary Table  3 ) between our three classifiers ranged from 87 to 94% in the van de Vijver's samples and 76 to 92% in the Desmedt's samples, with the Elastic net model having the highest level of correlation with the two other models in each dataset. The concordance rates between each of our classifiers and the 70-gene classifier ranged from 73 to 75% in the van de Vijver's samples and 63 to 66% in the Desmedt's samples. Such high concordance rates were rather expected between our three classifiers given the strong overlap between the three gene lists (Supplementary Table  2 ).
We also compared the genes included in our three models with the 48 unique named genes present in the 70-gene signature (Supplementary Table  4 ): four genes out of six (67%) of the CoxBoost classifier were included within the 48 genes (8%), six genes out of 18 (33%) of the LASSO classifier were included within the 48 genes (13%), and seven genes out of 29 (24%) of the Elastic net classifier were included within the 48 genes (15%). No GO analysis could be applied directly to our three classifiers because of the low number of genes included. We thus applied GSEA to each classification in each external validation dataset. In all analyses, the significant ontologies were associated with genes overexpressed in the predicted poor-prognosis class and by far the most represented ones were related to cell proliferation, including for example "cell cycle phase," "cell cycle process," "mitotic cell cycle," "M phase," "interphase," "DNA replication," and "mitosis.
In this paper, we have aimed at adding a step of classifier derivations from the selected models and we have studied the performance of such methods for developing an entire predictive signature for 5-year MFS from the van't Veer's dataset. The objective of this study was to conduct an unbiased comparison of three different prognostic classifiers derived using three different high-dimensional Cox regression models. Prior to analysis, an automatic selection procedure was used to determine the number of predictive genes to be included in each model to ensure the comparability between the different methods and ascertain that all methods are tuned to predict well on novel data. 8 We do not strictly recommend this approach, but the selection of the penalty via k-fold crossvalidation and setting other tuning parameters to default values permit a fair comparison between the different methods.
good prognosis). The three different strategies applied to the van't Veer's training set (78 samples) identified 19, 23, and 36 predictive genes, whose lists are almost nested. Five-year prediction accuracies computed in the internal validation set (n = 19) were close and quite good. The predictive ability and the transferability of the different modeling strategies for building an entire prognosis classifier were also validated in two additional independent datasets, on the basis of the 10,002 genes common to the van't Veer's, van de Vijver's, and Desmedt's datasets. The five-fold cross-validation procedure identified 6, 18, and 29 as the optimal number of predictive  genes for the CoxBoost, LASSO, and Elastic net methods, respectively. The three high-dimensional Cox regression models with automatic selection of the tuning parameter by crossvalidation led to the definition of prognostic signatures with similar predictive ability, added significant prognostic information to that provided by the classical parameters.