dataesr/scientific-paragraphs-categorization huggingface dataset:
(Manning et al. , Dataset # subgraphs before # subgraphs after reduction WebKB 30,868 10,113 67 % R8 39,428 11,373 71 % LingSpam 54,779 15,514 72 % Amazon 16,415 8,745 47 % Dataset # n-grams before # n-grams after reduction WebKB 1,849,848 735,447 60 % R8 1,604,280 788,465 51 % LingSpam 2,733,043 1,016,061 63 % Amazon 583,457 376,664 35 % Table 1: Total number of features (n-grams or sub- graphs) vs. number of features present only in main cores along with the reduction of the dimen- sion of the feature space on all four datasets. 2008, p. 281). Conversely, the macro-average F1- score takes into account the skewed class label dis- tributions by weighting each class uniformly. The statistical significance of improvement in accuracy over the n-gram SVM baseline was assessed us- ing the micro sign test (p < 0.
Genomics experiments are widely acknowledged to produce a huge amount of data to be analysed. The challenge is to extract meaningful biological context for proteins or genes which is currently difficult because of the lack of an integrative workflow that hinders the efficiency and the robustness of data mining performed by biologists working on ruminants. Thus, we designed ProteINSIDE, a free web service (www. proteinside. org) that (I) provides an overview of the biological information stored in public databases or provided by annotations according to the Gene Ontology, (II) predicts proteins that are secreted to search for proteins that mediate signalisation between cells or tissues, and (III) analyses protein-protein interactions to identify proteins contributing to a process or to visualize functional pathways.
To gain insight efficiency and robustness, free and commercial tools such as DAVID  [24] , BioMyn  [25] , ToppGene  [26] , Expander  [27] , PathwayStudio  [28] , or the web-based software package Ingenuity Pathway Analysis (http://www. ingenuity. com; Redwood City, CA, USA) were developed as one-stop portal in order to combine two or three analyses, mainly GO annotations, PPI interaction, and information retrieval. Most of them do not permit to address data mining from genomic and proteomic studies in ruminants, except AgBase  [29] , a curated genomic database providing functional annotations and information for agriculturally important species, among them bovine and ovine. None of these resources include the prediction of proteins that are secreted outside the cell as recommended for a computational analysis of secretome  [30].
ProteINSIDE provides graphical and interactive results viewable on the website or downloadable. ProteINSIDE extracts information for a list of genes or proteins ID from myriad data sources with a unique input. Thus, it will circumvent searching for the different ID required for queries with the multiple existing bioinformatics tools, and it will save analysis time for the biologists. We demonstrated the higher or similar performances of ProteINSIDE relatively to currently used web-services or dedicated resources by a bench test that has evaluated results from 1000 random proteins by species. The relevance of results produced by ProteINSIDE was also checked with data that were previously partly analysed  [3] [4] [5]. Lastly, we report the new knowledge related to adipogenesis, myogenesis, and the balance between both processes, provided by mining dataset from bovine foetal muscle and adipose tissues (AT) thanks to ProteINSIDE.
ProteINSIDE features ProteINSIDE is an online workflow with an interface devoted to user-friendly and fully customisable analyses from lists of proteins or genes ID. Registered users have access to a private session to run, save, and visualise their results. Unregistered users can use ProteINSIDE, there is no analyses manager and results are deleted each month. Uploaded data are encrypted to ensure confidentiality. ProteINSIDE is divided into three parts: the workflow, the database, and the web interface. The workflow is a combination of Perl and R scripts to query databases, recover protein data, perform calculations, and run algorithms for signal peptide predictions and network visualisation. The MySQL database aims to reduce server load and stores settings and results from queries. ProteINSIDE's database also stores available knowledge from major public biological databases.
Setting up a basic or a custom analysis/query ProteINSIDE performs either a "basic analysis" (in which settings are locked and the workflow acquires GO terms, signal peptide prediction, and PPi data from IntAct, UniProt, and BioGrid Flowchart of ProteINSIDE structure. The four modules to query the available biological information, annotate according to the GO, predict secreted proteins and visualize PPi, are either all run in the basic analysis or individually selected and run with specific settings in the custom analysis. The basic analysis runs ProteINSIDE with automatic settings. The custom analysis operates with the settings selected by the user: option to include GO Inferred from IEA codes (electronic annotation that are automatically unselected in the basic analysis), option to make GOTree chart networks with Cytoscape web, option to search PPi among the 31 databases proposed by ProteINSIDE, option to search PPi in other species using orthologous proteins, option to extend the PPi network with proteins that are not in the dataset, and option to choose the sensitivity to detect signal peptides with SignalP 4.
1. doi:10. 1371/journal. pone. 0128086. g001 respectively) or a "custom analysis" (in which the user chooses the modules, the settings, and databases for PPi) (Fig  1 ). Previously defined settings for a custom analysis can be used for new queries by registered users using the "preset analysis". To submit a basic or a custom analysis, users either directly paste a list of proteins or genes ID, or upload a file. Inputs can be protein (e. g. , ADIPO_HUMAN) or gene name (e. g. , ADIPO), protein accession number (e. g. , Q15848), or also gene identifier (e. g. , gi|62022275) from six species: Bovine, Human, Rat, Mouse, Ovine, and Caprine. A new analysis is run directly or is placed on a waiting list if the workflow is overloaded. 2. 1 Biological knowledge retrieval. Submitted ID, names, or accession numbers are compared to the ProteINSIDE database to ascertain a match with genes or proteins from Human, Rat, Murine, Bovine, Ovine, or Caprine species.
This is achieved by merging the local database, which is updated every month, with data from UniProt  [32]  and NCBI (Gene, Proteins, and HomoloGene;  [33] ) public databases for all known genes and proteins from these six species. 2. 2 Functional annotation according to GO. ProteINSIDE imports GO terms by querying the QuickGO database. QuickGO was chosen because of its daily update, accessibility and performances. In the basic analysis, ProteINSIDE only imports GO terms that have been selected by evidence codes (GO terms Inferred from Electronic Annotation (IEA) are excluded) and agreed by curator review. The use of IEA is a setting of the custom analysis. ProteINSIDE provides the number of genes or proteins annotated by a specific GO term relatively to the total number of genes or proteins within both the dataset (frequency within the list) and the GO (frequency within the genome).
The GO module of ProteINSIDE also analyses over-and under-represented terms to identify the most relevant and the most specific terms associated with the uploaded list, according to the functional enrichment first proposed by FatiGO  [34]. Briefly, the frequency of a specific GO within the dataset is compared to its frequency in the genome. For this, ProteINSIDE prepares a 2 by 2 contingency table for a specific GO, performs a Fisher exact test and reports significant over-expressed GO based on the p-value of the test. The test is performed by an R script as follow: mat < À matrixðcðA; B; C; DÞ; nrow ¼ 2Þ tes < À fisher:testðmatÞ where A is the number of proteins annotated by a specific GO term in the dataset, B the total number of proteins annotated in the dataset, C the number of proteins that are annotated by the specific GO term within the genome (also called "background of a GO term"), and D the total number of annotated proteins in the genome.
ProteINSIDE provides the significantly over-expressed GO based on both unadjusted p-value of the test and a p-value corrected for multiple testing correction by the FDR Benjamini & Hochberg (BH) method  [35]. The GO module also provides a view of network that links GO terms as a tree ancestor. The GO terms are linked by their parental association using ProteINSIDE database. ProteINSIDE database has its own version of ontology annotation from GO consortium  [36]  and QuickGO databases that relates: GO terms, biological functions, GO terms associations (relationships between terms only include "is_a" or "parts of" to select terms with a direct link), and the number of annotations by species. The "GOTree" view of ProteINSIDE is dynamic and only includes linked GO terms for a list of ID. The network is built with a custom version of Cytoscape web (v.
To identify proteins that are putatively secreted by non-classical protein exports or ER/Golgi-independent protein secretions, ProteINSIDE checks the subcellular location of proteins and compares their annotation relatively to our list of GO terms related to secretion. Lastly, to support these predictions (classical and non-classical ways) ProteINSIDE runs the TargetP software  [40]  that predicts the subcellular location of the proteins using the amino acid sequence. Tar-getP uses 4 cutoffs for its predictions: chloroplast (only for plant input), mitochondrion, secretory pathway (signal peptide), and other subcellular location. ProteINSIDE uses a preset cutoff option to get a significant prediction higher than 95% (according to TargetP tutorial: http:// www. cbs. dtu. dk/services/TargetP/instructions.
php). 2. 4 Protein-protein interactions and visualisation. PPi identification and visualisation within a network point out how various genes or proteins contribute to cellular or metabolic processes. ProteINSIDE uses Psicquic web service  [18]  to identify PPi and imports identified PPi by their "interaction detection methods" experimentally proven and agreed by curator review. Basic or custom analyses identify PPi within the uploaded dataset using the major preselected databases (IntAct, UniProt, and BioGrid chosen for the high data curation) or databases selected by the user (among 31 suggested by the PsicQuic web service). ProteINSIDE favours the use of multiple PPi databases in order to provide a more complete PPi network. In the custom analysis, ProteINSIDE identifies interactions between proteins from the dataset (core network) and proteins outside the dataset (extended network).
ProteINSIDE identifies PPi recorded either within the species of the analysis or within a different species (to be selected among the six). To choose another species, user have either to select the Blastp  [41]  setting to know the similarity between the sequences of uploaded proteins and their orthologs, or to use conserved proteins declared in the HomoloGene database (only for Human, Bovine, Mouse, and Rat). For the extended network, ProteINSIDE does not include links between proteins outside of the dataset themselves in order to not overload the network visualization with too many interactions. ProteINSIDE focuses only on PPi between proteins of the dataset and other proteins of the analysis species. If users wish to view and analyze a complete network with links between proteins outside the uploaded dataset, they have to download the identifiers of the PPi extension (available on the PPi table results page) and run a new analysis.
The workflow has been programmed using Perl (with CPAN modules (Comprehensive Perl Archive Network) and BioPerl  [43] ) and R scripts. The database was made in MySQL. By using these most common web dynamic programming languages, ProteINSIDE is completely available on any operating system using an internet browser (Internet Explorer, Firefox, Chrome, Safari. ). To ensure the sustainability of ProteINSIDE, we have set up an automatic update of the database. A program updates each month the biological information of the database of ProteIN-SIDE by comparison with the current and free NCBI and UniProt databases resources. Moreover, it gathers the information from the databases QuickGO and Amigo in order to update the GO term database of ProteINSIDE (function and annotated genes expected by GO term for each species) or to add new GO terms.
The workflow update is done manually. If a program requires an update, it is set up and tested locally to avoid conflicts with running analyses of users. When the update is applied to ProteINSIDE: users can press the button "reload analysis" (green arrow) to restart previous analysis and to benefit to the new version of the workflow. Each new version of ProteINSIDE's database or workflow is informed on the website by a new or by a message in the "About" section and the "User's main page". Users should be aware that results obtained with 2 different versions of ProteINSIDE may be not reproducible because of the deletion of obsolete data and the use of new ones.
Datasets To test ProteINSIDE's performances, we have created six datasets made of 1000 random proteins for each species (S1 Table lists the six samples files). These proteins have been selected using a script which queries randomly 1000 reviewed proteins for a species on the UniProt database. We have made separate custom analysis to test each part of the workflow using these datasets of 1000 random proteins by species. We have tested GO annotation using two customs analyses: with and without electronic annotation (option IEA). The prediction of secreted proteins has been bench tested using a custom analysis with SignalP prediction and GO annotation. Lastly, PPi were tested using four customs analyses: two analyses have queried the 3 databases (BioGrid, Uniprot, and IntAct) selected by default in the basic analysis to identify core and extended networks, and two analyses have queried 9 other databases (BioGrid, IntAct, MINT, MatrixDB, STRING, Reactome, InnateDB-IMEx, UniProt, and I2D-IMEx, among those to be chosen by the user) to identify core and extended networks.
-indicates that the resource does not provide annotation without IEA. doi:10. 1371/journal. pone. 0128086. t003 may come from the lack of a recent database update for DAVID and the consequent missing of biological information (Table  1 ). For each uploaded ID, ProteINSIDE obtained and summarized, as a downloadable table, the gene or protein ID, gene and protein names, the protein function, the gene chromosomal location, information on tissues expression, the cellular location, orthologous ID, and the FASTA sequence of the protein. These results are directly viewed on the "ID resume" web page of ProteINSIDE, and each protein or gene ID are linked to corresponding UniProt and NCBI web pages. A part of these biological data is also provided by DAVID, BioMyn, and AgBase. 1. 2 Gene Ontology annotation.
ProteINSIDE annotated proteins or genes ID with GO terms selected by evidence codes (IEA are excluded) and agreed by curator review in the basic analysis, and additionally with GO terms from IEA as a setting of the customs analysis. As Pro-teINSIDE, AgBase allowed unselecting IEA, while DAVID and BioMyn use IEA by default. Thus, results from the GO module of ProteINSIDE for 1000 random proteins ID were compared to DAVID and BioMyn results with IEA, and to AgBase results with and without IEA (Table  3 ). On average 90% of proteins ID from Human, Rat, or Mouse annotated by ProteINSIDE were also annotated by DAVID. However, the percent of proteins annotated both by ProteIN-SIDE and DAVID decrease to 86%, 40%, and 0. 6% for Bovine, Ovine, and Caprine ID, respectively. The numbers of GO terms from DAVID were around 52% higher for Human, Mouse, Rat, and Bovine proteins but 86% and 48% lower for Caprine and Ovine proteins, respectively, when compared to ProteINSIDE.
Consequently, around 64% of GO terms from ProteINSIDE were common to those provided by DAVID for five species. On average, 85% of Human proteins were annotated both by ProteINSIDE and BioMyn with 50% of GO terms provided by the two resources. For Bovine and Ovine proteins, ProteINSIDE annotated more proteins with less GO terms than AgBase in analyses without IEA, while AgBase annotated more proteins with more GO terms when IEA annotations were used. In both analyses, GO terms provided by ProteINSIDE were all retrieved with AgBase. The annotation differences between ProteIN-SIDE and DAVID, or to a lesser extend BioMyn, may result from the lack of a recent update of the databases used. For example, from our lists of proteins, DAVID had provided GO terms that were declared obsolete by the GO consortium (GO:0006096 previous term was "Glycolysis", replaced the 29 th march 2014 by "Glycolytic process") that thus could not be retrieved by resources such as ProteINSIDE and AgBase (that integrate the GO consortium updates).
It is noteworthy that AgBase provided reliable GO annotations for two ruminant species, probably because of their own curated database that is also monthly updated. Thus, both AgBase and ProteINSIDE perform GO annotations for bovine and ovine, while only ProteINSIDE annotates ID from Caprine species. It is noteworthy that ProteINSIDE annotated on average 45% more proteins with 46% more GO terms when using IEA by comparison without IEA. The results are viewed on the "GO" web page of ProteINSIDE as tables, diagrams, and GOTree charts. Unlike the other available tools, ProteINSIDE summarized main results of GO as GOTree charts which are ordered tree layout networks that link related GO terms (Fig  2 ). 1. 3 Secreted proteins. Whatever the species, 95% of signal peptides predicted by ProteIN-SIDE thanks to SignalP were also predicted by PrediSi and Phobius (Fig  4 ) for 1000 random protein ID.
However and as expected, the number of signal peptides predicted by ProteINSIDE was 49% and 24% lower than those predicted by PrediSi and Phobius, respectively. This may result from the known higher false positive rate of prediction by PrediSi and Phobius (52% and 16% according to  [23] , respectively) than SignalP 4. 1 (6%). As an example of false positive, ALKBH5 was predicted as a secreted protein by Phobius and PrediSi but not by ProteINSIDE, while this is a nuclear protein according to UniProt database. An added-value of ProteINSIDE is the use of GO terms and TargetP, both to reinforce the prediction of proteins that are secreted (Table  1 ). By adding these two methods, 48% (Fig  4 ) and 12% (in average for the six species, data not shown) of predicted secreted proteins with and without signal peptide were confirmed by GO terms and TargetP prediction, respectively.
This two-step analysis improves the reliability of proteins proposed to be secreted. Moreover, unlike other available resources ProteINSIDE predicts proteins exported out of eukaryote cells by non-classical secretory pathways (without signal peptide). However, all of these predictions remain to be confirmed by new or already published experiments. Lastly, GO terms confirmation can be improved by the use of IEA codes for annotations. Results are viewed on the "Secreted Protein" web page of ProteINSIDE as two main tables: the first table listed proteins secreted by the classical Golgi/ER-dependent secretory pathway thanks to a signal peptide and the second table listed proteins that are predicted to be secreted by non-classical secretory pathways. 1. 4 Protein-Protein interactions. The number of PPi identified by querying 3 major (UniProt, BioGrid, and IntAct preselected in the basic analysis) or 9 widely used PPi databases Venn diagrams of predicted signal peptides from amino acid sequence of 1000 proteins.
PPi were searched between proteins within (core network) and outside (extended network) the dataset. As expected, the number of PPi identified was higher when 9 rather than 3 databases were queried both for core and extended networks (except sheep and goat, Table  4 ). Martha and co-workers  [46]  have previously emphasized the need for a trade-off between the coverage and the reliability of PPi. The maximal coverage can be achieved as we have done by using a traditional union approach for PPi integration, which might however increase false positive PPi. We attempted to minimize the number of false positive by selecting PPi that have been experimentally detected only. With this caution, ProteINSIDE constructs networks with PPi from multiple public PPi databases in order to increase the recovery of species interactome: better informed network with more curated PPi.
It is noteworthy that none PPi were identified between Ovine or Caprine ID, which is consistent with the lack of experiments that search for PPi in these species. However, ProteINSIDE database is monthly updated and latest experimentally proven PPi from these species would be identified in the future. Because PPi in ruminant remains poorly recorded, ProteINSIDE automatically constructs networks from ruminant ID within well-studied species chosen either in a blind way by the user or from results of alignments (Blastp) between the sequences of uploaded proteins and their orthologs. It may be useful for biologists working with ruminant species to identify the relationships among different proteins in order to understand the contribution of genes or proteins to pathways or cellular processes. On the "Protein Interactions" web page of ProteINSIDE, PPi are viewed as tables of listed PPi and as dynamic networks.
Analysis of proteomics data from bovine adipose and muscle tissues using ProteINSIDE Custom analyses were proceeded with 143 and 120 proteins ID identified by proteomics from perirenal AT  [4]  and Semitendinosus muscle  [3, 5]  sampled from the same bovine foetuses at key developmental ages. With these datasets, we aimed firstly to check for the relevance of Pro-teINSIDE results and secondly to focus on new knowledge provided by ProteINSIDE by comparison with our previous analyses  [3] [4] [5]. Settings used for custom analyses were: Gene Ontology with IEA, prediction of secreted proteins with default settings, and PPi research on 9 widely used databases (BioGrid, IntAct, MINT, MatrixDB, STRING, Reactome, InnateDB-I-MEx, UniProt, and I2D-IMEx). 2. 1 Foetal Bovine AT features. ProteINSIDE successfully uploaded and provided a fast overview of the biological information available in UniProt and NCBI databases for 143 proteins that were identified in the AT at each foetal age.
Thus, by using ProteINSIDE, we have identified 13 proteins potentially secreted thanks to a signal peptide and 11 proteins potentially secreted by non-classical ways. It is noteworthy that these proteins could be synthesized by either AT such as adiponectin  [4]  or other tissues and taken up by AT, that remains to be questioned by the search of their mRNA in AT in available published results or by new experiments. ADIPOQ 1,2,3 ADIPOQ 1,2,3 ADIPOQ NDUS3 ALB 1,2,3 APOA1 ERLIN2 2,3 AFP 2,3 AFP NDUS8 APOA1 1,2,3 ALB NDUFA10 GSN 1,2,3 PDIA3 SERPINA1 1,2,3 GARS 1 SERPINA1 APOA1 1,2,3 SERPINA1 1,2,3 APOA2 1,2,3 P4HB 1,2,3 FGG 1,2,3 PDIA3 1,2 TTR 1,2,3 ALB 1,2,3 AFP 1,3 TF 1,2,3 PCCB COL6A2 1,2,3 HSP90B1 2,3 PDIA3 2,3 The PPi module of ProteINSIDE had revealed that 107 proteins of the dataset were linked by 300 interactions (Fig  5 ).