dataesr/scientific-paragraphs-categorization huggingface dataset:
Other papers reporting interaction data for the extracellular matrix (ECM) full-length biomolecules and fragments (matricryptins) involved in angiogenesis, ECM maturation and neurodegenerative diseases are also curated. Previous curation performed by MatrixDB was at the MIMIx level (The minimum information required for reporting a molecular interaction experiment, (4)). MatrixDB now performs deep curation at the IMEx level and uses the web-based curation tool developed by IntAct, which is a common curation platform for 11 molecular interaction databases  (5). Other ECM-related databases collect data on the sequences or mutations of extracellular proteins or on a single extracellular protein family. For example, ElastoDB is a repository for elastin sequences  (6) , the laminin database stores information concerning laminins in health and disease (7), COLdb links mutations and genetic data to molecular function in fibrillar collagens  (8)  and the COL7A1 mutation database is focused on collagen VII  (9).
To the best of our knowledge MatrixDB is the only general-purpose database curating and storing interactions taking place in the ECM and at the cell surface. MatrixDB is not restricted to protein-protein interactions like most other databases, but also focuses on protein-glycosaminoglycan interactions. Glycosaminoglycans are complex polysaccharides that are abundant in the ECM and at the cell surface, where they play key roles in adhesion and signaling  (10). MatrixDB data have been used to build interaction networks of extracellular proteins  (11, 12) , glycosaminoglycans (heparin and heparan sulfate,  (13, 14) ), proteoglycans  (15) , basement membrane (a specialized ECM, (1)), the ECM  (16)  or physiological process (aging,  (17) ). Several protein interaction network visualizers have been developed recently such as the Web-based Protein Interaction Network Visualizer, which allows interaction data to be explored interactively on line  (18) , or mentha, a resource for browsing and filtering integrated protein interaction networks  (19).
Sources of biomolecules The biomolecules stored in MatrixDB are proteins, bioactive fragments of ECM biomolecules called matricryptins  (20) , glycosaminoglycans, synthetic peptides, ions, lipids and other inorganic compounds. Proteins are identified by their UniProtKB primary accession numbers  (21) , while custom MatrixDB identifiers are used for all other types of biomolecules: PFRAG x s for bioactive fragments (x being a number and s a species), GAG x for glycosaminoglycans, SPEP x for synthetic peptides, CAT x for ions, LIP x for lipids and INORG x for other inorganic compounds. In addition, a number of ECM proteins and receptors (e. g. collagens, laminins, thrombospondins and integrins) are composed of several polypeptide chains and the native protein molecules are thus obligate complexes.
They are termed MULT x s (for multimers, again x is a number and s a species). Protein data were imported from UniProtKB, and enriched by parsing additional annotations from Gene Ontology, InterPro, Pfam and UniGene (http://www. ncbi. nlm. nih. gov/unigene). The custom MatrixDB biomolecules are cross-referenced to relevant external databases when available. We reference the PRO feature of UniProtKB for bioactive fragments, the Chemical Entities of Biological Interest (CheBI,  (22) ) for glycosaminoglycans, ions, lipids and other inorganic compounds, the Lipid bank (http://www. lipidbank. jp/) for lipids and the complex portal (http:// www. ebi. ac. uk/intact/complex/) for multimers. Although MatrixDB is focused on Homo sapiens, relevant ECM interactions are sometimes experimentally demonstrated with biomolecules from other organisms.
Sources of interaction data The content of MatrixDB has been updated with new interaction data manually curated by the MatrixDB team, and by importing interaction data from four interaction databases of the IMEx consortium via The Proteomics Standard Initiative Common QUery InterfaCe (PSICQUIC), a community standard for computational access to molecular-interaction data resources  (25). The queried databases are IntAct  (26) , MINT  (27) , DIP  (28)  and InnateDB  (29). All interactions involving at least one biomolecule from our 'secreted' or 'ECM' lists were imported, provided that the other partner originated from human, murine, bovine, chicken or zebrafish. Experimental details such as the participant features (e. g. binding sites, effect of mutations) and interaction parameters (e. g.
affinity, kinetics) were imported when available. In the previous version of MatrixDB, the use of homology to infer human interactions from interactions experimentally identified using non-human biomolecules was somewhat confusing. This has been clarified in the new version of the database, where a clear distinction is made between interactions supported by experimental evidence, tagged as 'genuine', and those inferred by homology, tagged as 'inferred' and linked back to the non-human interaction. Data from the Human Protein Reference Database (HPRD)  (30)  were not included in the update because curated HPRD interaction data are limited to interactor names and experiment type, which is not reported with the controlled vocabulary used by the IMEx databases (Ontology Lookup Service,  (31)  whereas MatrixDB aims at providing a rich and detailed view of ECM interactions.
Furthermore HPRD has not yet implemented the PSICQUIC service we use to retrieve extracellular interaction data in other databases. Interaction data from BioGRID  (32)  were not included because the BioGRID database captures interactions at the gene level. Although a mapping to the canonical protein is provided, interactions involving a specific protein isoform and/or a processed protein fragments (matricryptins), which play crucial roles in the ECM, cannot be represented. In the current release MatrixDB contains 904 interactions supported by 1244 experiments, which have been manually curated from 237 publications, compared to 490 interactions supported by 847 experiments in the previous version of the database (2). This is the MatrixDB 'core' data set. Furthermore, 8947 ECM-related interactions supported by 13851 experiments have been imported, resulting in the 'PSICQUIC-extended' data set.
Both data sets are freely available in MITAB 2. 7 format on the 'Download' page. MatrixDB now contains 15095 experiments supporting 9851 interactions. These include 9262 proteinprotein interactions, 139 protein-multimer interactions, 175 protein-glycosaminoglycan interactions, 22 proteinion interactions, 5 protein-lipid interactions and 5 proteinsynthetic peptide interactions compared to 2174 interactions available in the previous release.
Biomolecule report page Biomolecule data are displayed in several boxes on a single page (Figure  1A ). A first box containing the MatrixDB identifier is always displayed, whereas the other boxes (biological function and location data, complex information, cross-references, domain annotation and 3D structure) are displayed only when the corresponding information is available (Figure  1A ). The individual polypeptide chains multimeric proteins referred to as multimers or complexes (e. g. collagens, laminins, thrombospondins) and their stoichiometry are listed in the 'Complex composition' box with a link to the polypeptide report page. Protein domains (Pfam,  (33)  and Interpro, (  34 )) are listed. The 3D structure of the protein, selected from the PDB entries selected by UniProtKB  (35) , is displayed and can be rotated.
The cellular and tissue location of the biomolecule is annotated using UniProtKB keywords and GO 'Cellular component' terms. The partners of the biomolecule, their identifier, their species and the number of experiments supporting their interaction with the biomolecule are listed (Figure  1B ). They can be sorted alphabetically by their names, ranked by the number of experiments supporting the interaction and filtered according to their name or species. The partners can be added to a cart for later building of interaction networks as described below. The total number of additional partners available in all the PSICQUIC-enabled databases is also indicated, based on an on-the-fly query (Figure  1B ). Expression data imported from UniGene are displayed as a bar chart for body sites (tissues), developmental stages and health states with a focus on tumors.
Experiment report page It comprises the interaction identifier, the name of the database which curated the interaction data, the interaction type, the interaction detection method, kinetics and affinity of the interactions when available and the PubMed reference of the publication(s) supporting the interaction (Figure  2 ). These information are provided for all interactions (i. e. those curated by MatrixDB and those imported from IMEx databases via PSICQUIC). Further information on participant features (e. g. biological role, experimental role, detection method, binding sites and effects of mutations) and on interactions (kinetics, affinity) are displayed when available.
The process can be repeated iteratively to increase network coverage. Conversely, the user can delete biomolecules and interactions from the network. The experiment report page. As in the biomolecule report page, the top bar allows to scroll to subsections, displays the current cart size and provides direct access to iNavigator. The gray box displays information about the experiment (interaction type, detection method, external references and supporting information). When available, kinetics and affinity data are also shown. Specific information concerning each partner is presented below in blue boxes (e. g. binding sites, biochemical modifications and mutations).
Querying displayed interaction networks The user can search for free text, biomolecule names or identifiers (UniProtKB, CheBI and MatrixDB, EBI complexes) using the search bar displayed at the top of the network visualizer. Matching biomolecules are highlighted in red and yellow. The iNavigator can also search for biomolecules based on the existence -or absence -of partners after selection and filter applications of the initial network. The iNavigator uses graphical layers to project information on the network. In particular, tissue and/or health state specific sub-networks can be built thanks to the 'Filter network widget' found below the 'Tabular network widget' and 'Cart' icons (left-hand side of the screen). The user can select one or more health states and tissues of interest and specify an expression threshold in transcripts per million for each selected tissue (Figure  4 ).
Biomolecules expressed above the threshold in all selected tissues are displayed in green, while others are displayed in red. Biomolecules lacking expression data may be included or not in the interaction network thanks to the MDE (Missing Data Elements) checkbox. Using the 'Filter network widget', one can also build a network comprised of interactions identified by one or several experimental methods. Interactions detected by at least one of the selected methods are colored in green, whereas those detected by other methods are colored in red. Red biomolecules and/or interactions are masked when clicking 'Apply'.
Implementation iNavigator has been developed using two well-established JavaScript libraries, namely D3 (37) and jQuery (https:// jquery. com/). The tables of the report pages use the DataTables plugin (http://www. datatables. net/) and the visualization component for 3D structures of biomolecules relies on GLmol (http://sourceforge. jp/projects/webglmol/). iNavigator visualization widgets were built as individual reusable components, following the BioJs open-source standard for biological data visualisation  (38). Additionally, iNavigator strives to minimize response time. In particular, all data queries are performed server-side against our local MatrixDB database, rather than querying PSICQUIC in real time. We applied the Ajax methodology to decouple the network's construction and the retrieval of data associated with biomolecules and interactions.
This ensures that networks can be displayed and modified via iNavigator while data are still being retrieved in the background. All modifications (e. g. filters) made on the networks are reversible. In- The network filter widget. The widget displays two filters: 'expression level' (in transcripts per million) for biomolecules, and 'detection method' for interactions. Checking several boxes imposes all criteria to be met (i. e. logical AND). An expression level (TPM) threshold can be specified, and applies to all selected tissues/health state/developmental stages. The widget uses a color code to preview the effect of a filter on network elements: green elements meet the criteria while red ones do not. Biomolecules that lack expression data are colored in cyan and called Missing Data Elements (MDE).
Figure2. The experiment report page. As in the biomolecule report page, the top bar allows to scroll to subsections, displays the current cart size and provides direct access to iNavigator. The gray box displays information about the experiment (interaction type, detection method, external references and supporting information). When available, kinetics and affinity data are also shown. Specific information concerning each partner is presented below in blue boxes (e. g. binding sites, biochemical modifications and mutations).
ACKNOWLEDGEMENTS We thank  Christophe Blanchet  and  Alexis Michon  ( FR 3302 ,  Institut de Biologie et Chimie des Protéines, Lyon, France ) for helpful discussions and technical assistance,  Sylvain D. Vallet  for his contribution to the curation process,  Marine Dumousseau  ( European Bioinformatics Institute, Hinxton, UK ) for her help regarding data exchange and  Sandra Orchard  ( European Bioinformatics Institute, Hinxton, UK ) for her expert assistance with curation issues.
Rankcluster is the first R package proposing both modeling and clustering tools for ranking data, potentially multivariate and partial. Ranking data are modeled by the Insertion Sorting Rank (ISR) model, which is a meaningful model parametrized by a central ranking and a dispersion parameter. A conditional independence assumption allows to take into account multivariate rankings, and clustering is performed by the mean of mixtures of multivariate ISR model. The clusters' parameters (central rankings and dispersion parameters) help the practitioners in the interpretation of the clustering. Moreover, the Rankcluster package provides an estimation of the missing ranking positions when rankings are partial. After an overview of the mixture of multivariate ISR model, the Rankcluster package is described and its use is illustrated through two real datasets analysis.
Introduction Ranking data occur when a number of subjects are asked to rank a list of objects according to their personal preference order. Such data are of great interest in human activities involving preferences, attitudes or choices like Psychology, Sociology, Politics, Marketing, etc. For instance, the voting system single transferable vote, occurring in Ireland, Australia and New Zealand, is based on preferential voting (Gormley and  Murphy, 2008). In a lot of applications, the study of ranking data discloses heterogeneity, due for instance to different political meanings, different human preferences, etc. A clustering analysis is then a useful tool for statistical exploration of such datasets, by characterizing the datasets by the mean of a reduced number of clusters of similar ranking data.
In the present work, the clusters will be characterized by a median ranking (location parameter) and by a dispersion parameter. The interpretation of these two parameters for each cluster will produce a comprehensive and synthetic analysis of the whole dataset. In the present paper two studies in sociology and in sport are carried out. In the sociology study, several students were asked to rank five words according to strength of association (least to most associated) with the target word "Idea": Thought, Play, Theory, Dream and Attention. The cluster analysis of the resulting rankings from this study allows to characterize the population of students by exhibiting three clusters of students, that can be interpreted (thanks to the parameters specific to each cluster) as follows: scientific students, literary-minded students and a third group of students in an intermediate position between scientific and too literary-minded.
The heterogeneity of the rank population is modeled by a mixture of ISR whereas conditional independence assumption allows the extension to multivariate rankings. Maximum likelihood estimation is performed through a SEM-Gibbs algorithm, in which partial rankings are considered as missing data, what allows to simulate them during the estimation process. This algorithm has been implemented in C++ and is available through the Rankcluster package for R, available on the CRAN website and presented in depth in the sequel of this paper. The paper is organized as follows: the next section briefly presents the clustering algorithm proposed in  Jacques and Biernacki (2012). Then, the following sections describe the existing R packages dedicated to ranking data, and the functionalities of the Rankcluster package.
The Rankcluster package is then illustrated through the cluster analysis of two datasets: 1. Fligner and Verducci's words univariate dataset without any missing ranking positions  (Fligner and Verducci, 1986) , and 2. the rankings of the Big Four of English Football (Manchester United, Liverpool, Arsenal and Chelsea) according to the Premier League results and to their UEFA coefficients between 1993 and 2013 (bivariate dataset with missing ranking positions). The last section concludes the work.
The univariate ISR model Rank data arise when judges or subjects are asked to rank several objects O 1 ,. , O m according to a given order of preference. The resulting ranking can be designed by its ordering representation x = (x 1 ,. , x m ) ∈ P m which signifies that Object O x h is the hth (h = 1,. , m), where P m is the set of the permutations of the first m integers, or by its ranking representation x -1 = (x -1 1 ,. , x -1 m ), which contains the ranks assigned to the objects and means that O i is in x -1 i th position (i = 1,. , m). In the Rankcluster package, the ranking representation is used, but users working with the ordering representation can use a function implemented in the package in order to convert their data from one representation to another one. Based on the assumption that a rank datum is the result of a sorting algorithm based on paired comparisons, and that the judge who ranks the objects uses the insertion sort because of its optimality properties (minimum number of paired comparisons),  Biernacki and Jacques (2013)  stated the following so-called ISR model: p(x; µ, π) = 1 m! ∑ y∈P m p(x|y; µ, π) = 1 m! ∑ y∈P m π G(x,y,µ) (1 -π) A(x,y)-G(x,y,µ) , (1) where • µ ∈ P m , the modal ranking, is a location parameter.
Existing R packages for ranking data To the best of our knowledge, there exists only two packages dedicated to the analysis of ranking data, available on the CRAN website, but their functionalities are significantly limited in comparison to our package Rankcluster, as we discuss now: • pmr package for R  (Lee and Yu, 2013) : provide some descriptive statistics and modeling tools using classical rank data models for full univariate ranking data: Luce models, distance-based models, and rank-ordered logit (refer to  Marden (1995)  for a description of these models). Visualization of ranking data using polytopes is also available for less than four objects to rank (m ≤ 4). • RMallow package for R: suppose to perform clustering of univariate ranking data using mixture of Mallows model  (Murphy and Martin (2003) ).
The main function: rankclust() The rankclust() function performs modeling or cluster analysis of a dataset of rankings. For cluster analysis, the number of clusters can be specified by the user, or selected by the BIC  (Schwarz, 1978)  (or ICL  (Biernacki et al. , 2000) ) criterion in a list of possible numbers provided by the user. By default, the number of clusters is equal to one, and consequently modeling of the dataset is performed (i. e. a single homogeneous multivariate ISR model is fitted to the data). Main inputs and outputs are described below. This function has only one mandatory argument, data, which is a matrix composed of the n observed ranks in their ranking representation. For univariate rankings the number of columns of data is m (default value of argument m). For multivariate rankings, data has m 1 +.
Companion functions In addition to the main function, rankclust(), several companion functions are available in Rankcluster: • convertRank(): converts ranking representation x -1 of a rank to its ordering representation x, and vice-versa since x • x -1 = x -1 • x. • distCayley(), distHamming(), distKendall(), distSpearman(): compute usual distances between rankings (refer to  Marden (1995) ) for either ranking or ordering representation. • frequence(): transforms a raw dataset composed of a matrix of ranks (one rank per line, with possibly equal lines if the same rank is observed several times) into a matrix rank/frequency containing in line each different observed ranks and one additional last column with the frequency of observation of these ranks. Conversely, unfrequence() transforms a rank/frequency dataset in a raw dataset, as requested in input argument of rankclust().
• khi2(): performs a chi-squared goodness-of-fit tests and return the p-value of the test (refer to Biernacki and Jacques (2013) for details). • kullback(): estimates the Kullback-Leibler divergence between two ISR models. • simulISR(): simulates a univariate and unimodal dataset of full rankings according to the ISR model. • probability(): computes the probability of a ranking (multivariate or not) according to the ISR model.
Rankcluster through examples This section illustrates the use of the rankclust() function on two real datasets. The first one, words, is a well-known dataset in ranking study, due to  Fligner and Verducci (1986) , which consists of words associations by students. The second one, big4 consists of the rankings of the Big Four of English Football (Manchester United, Liverpool, Arsenal and Chelsea) according to the Premier League results and to their UEFA coefficients between 1993 and 2013.
The words dataset This dataset was collected under the auspices of the Graduate Record Examination Board  (Fligner and Verducci, 1986). A sample of 98 college students were asked to rank five words according to strength of association (least to most associated) with the target word "Idea": 1 = Thought, 2 = Play, 3 = Theory, 4 = Dream and 5 = Attention. R> install. packages("Rankcluster") R> library(Rankcluster) R> data(words) Using the rankclust() function, a clustering with respectively 1 to 5 clusters is estimated: R> res=rankclust(words$data,m=words$m,K=1:5,Qsem=1 ,Bsem=1 ,Ql=5 ,Bl=5 ,maxTry=2 ,run=1 ) The number of SEM-Gibbs iterations (Qsem) has been set to 1000, with a burning phase of 100 iterations (Bsem). For likelihood approximation the numbers of iterations (Ql and Bl) have been divided by two.
The big4 dataset In the two last decades, the English football has been dominated by four clubs, forming the "Big Four": Manchester United, Liverpool, Arsenal and Chelsea. In this application, we analyse the rankings of these four teams at the English championship (Premier League) and their rankings according to the UEFA coefficients. This coefficient is an European statistic on football teams based on the results of European football competitions and used for ranking and seeding teams in international competitions. The big4 dataset, available in the package, is composed of Premier League rankings and UEFA rankings from 1993 to 2013, in ranking notation (club "1" is Manchester United, "2" is Liverpool, "3" is Arsenal and "4" is Chelsea). In 2001 Arsenal and Chelsea had the same UEFA coefficient and then are tied for the first ranking dimension.