dataesr/scientific-paragraphs-categorization huggingface dataset:
WEB INTERFACE Users can access 'Target' and 'Ligand' lists and search tools directly from the portal homepage, as well as from the navigation bar at the top of every subsequent webpage. Each class of target (e. g. transporters, enzymes) is listed according to protein family (e. g. ATPbinding cassette family, amino acid hydroxylases). The portal is designed to provide users with access to two views of pharmacologically relevant data on the targets in the database. The organization and content of these two complementary views is described below: (1) Users are initially presented with concise, searchable overviews of the properties of each family of targets. Data on all members of a target family, or subfamily, are presented on a single webpage (Figure  3 ). The page for each target family includes a brief overview of the properties of the target group.
Details are provided on approved nomenclature (where applicable, approved by NC-IUPHAR) and synonyms, human, mouse and rat gene names and links to the HGNC, MGD, RGD, Ensembl and UniProt databases. Quantitative data are provided on recommended ligands classified by their mode of action (e. g. agonists, antagonists, substrates, inhibitors and radiolabelled ligands) and other information specific to the class of target (e. g. the signal transduction mechanisms used by GPCRs, or the biophysical properties of ion channels). Overall, the data focus on human proteins and include only key pharmacological agents, chosen because they are likely to be the most useful in the laboratory (i. e. they are selective and available by donation, or from commercial sources). A list of review articles recommended as further reading, key references and additional commentary (highlighting, for example, where species differences, or ligand metabolism, are potential confounding factors) are also provided.
These pages are designed to serve as an introduction to a family of targets and are a useful entry point into the literature for newcomers to a particular field. (2) From the family overview pages, users can then navigate (via the 'More detailed page' links, see Figure  3 ) to database pages with more in-depth information for a subset of important targets, providing expanded views of the pharmacology, genetics, functions and pathophysiology. These include a longer introduction to the family and separate pages providing a comprehensive description of each target and its function, with information on protein structure, ligand interactions, signalling mechanisms, tissue distribution, functional assays and biologically important variants (e. g. single nucleotide polymorphisms and splice variants).
Reported ligand interactions may include endogenous ligands, current and historical licensed and experimental drugs, and available radiolabelled ligands, along with information on their actions (e. g. agonist, allosteric modulator, inhibitor) and quantitative data, where possible from multiple literature sources. Comparative data for mouse and rat species are also listed. In addition, the phenotypes resulting from altered gene expression (e. g. in genetically altered animals or in human genetic disorders) are described. An extensive set of links is provided to other resources including protein, gene, structure, disease and drug target databases. Family-specific information and database links are also provided, such as Enzyme Commission (EC) numbers and links to the KEGG BRITE hierarchy describing enzymatic reactions  (28).
For further details on the types of information that are provided in the detailed view see previous publications  (3, 15, 29). All literature citations in both views are linked to PubMed, and all ligand entries are linked to individual ligand pages providing additional information (as described in the section on 'CONTENT AND DATA CURATION' above). The interface includes a simple search box where users can enter keywords such as ligand or target names, and advanced search tools which allow searches by specific database field, database identifier (e. g. Ensembl ID), chemical identifier (e. g. standard InChIKey, CAS registry number) or PubMed identifier. Chemical structure searches can also be performed by providing a structure in SMILES format, or drawing a chemical structure using  the structure editor.
The search tool can perform exact match, substructure, similarity and SMARTS-pattern searches (http://www. daylight. com/dayhtml/doc/theory/ theory. smarts. html). The chemical structure editor is also accessible from ligand pages; clicking on the ligand image loads the structure into the editor where it can be modified and used to search the database. Search results indicate which database fields matched the query term, and links are provided to the relevant database entries. Extensive help pages and a tutorial on how to use the resource are also provided. The help page can be accessed via linked icons within database fields as well as from the navigation menu and home page. The help page includes definitions of terms used to describe the data displayed on the site, in addition to providing a detailed guide to using the various search functions.
COMPARISON WITH OTHER RESOURCES There are other databases that have a degree of conceptual and content overlap with the Guide to PHA-RMACOLOGY, some of which are included in this issue. Of these, ChEMBL, DrugBank and Therapeutic Target Database (TTD)  (30)  are the closest. However, the Guide to PHARMACOLOGY differs from these resources in a number of important ways. Firstly, we restrict the range of protein targets and ligands to those most relevant to therapeutics and drug discovery, chosen with the exercise of curatorial judgement and backed by our network of experts, with a focus on the quality and depth of annotation. Secondly, this is subject to review and quality control, not only by our international expert committee members operating as a de facto network of 'super-curators', but also via user feedback.
Thirdly, we curate activity data for research compounds from primary literature sources, including posters and patents, rather than from review articles, with a focus on the interactions of each compound with its data-supported primary target (e. g. Angiotensin-converting enzyme (ACE) for captopril). Fourthly, the data can be annotated with free-text comments that would otherwise not easily fit into database schema. These include information on alternative isomers and salt forms. An example here are the eight approved drug-prodrug pairs for ACE inhibitors that present a particular curatorial challenge (e. g. see http://www. guidetopharmacology. org/GRAC/LigandDis-playForward?ligandId=6352). These 16 structures are not both explicitly linked and activity-mapped in other databases. Another example that illustrates the differences between the three databases is atorvastatin.
In the Guide to PHARMACOLOGY (http://www. guidetopharmacology. org/GRAC/LigandDisplayForward?tab=biology&ligan-dId=2949), there are three activity mappings between this ligand and the primary drug target hydroxymethylglutaryl-CoA reductase (HMGCR) with both a K i (14 nM) and an IC 50 for human (8 nM), together with an IC 50 for rat  (1. 16 nM). The equivalent DrugBank entry (DB01076) is mapped to 3 targets, 11 enzymes and 9 transporters, but these include associations from the literature that are not all supported by directly measured molecular interactions. The ChEMBL entry (CHEMBL1487) is assay-mapped to 117 proteins and lists 217 IC 50 values, including proteins in the DRUGMATRIX screen and some antimalarial parasite results. There are four IC 50 values for the rat and three for the human enzyme.
SUMMARY AND FUTURE DIRECTIONS Our goal is to complete a stringently curated direct mapping (where the primary literature data permits) between chemical structures and their primary molecular targets, initially for targets of approved drugs, but extending this to clinical and research targets. Published listings and the exact definitions for these categories vary widely, but indicate a range of 200-300 for the former and 500-1000 for the latter  (32) (33) (34) (35) (36). Possible reasons for disparities in these numbers are indicated in database comparison reports  (24, 31). We are also in the process of updating our ligand structure submissions to PubChem, facilitating UniProt cross references for their targets and reviewing new information sources for possible inclusion. The creation of the new portal reflects our intention to develop the resource into a comprehensive online guide, which will include educational resources, and to produce a 'Concise Guide to PHARMACOLOGY', to be published in PDF format at two yearly intervals, as a supplement to the British Journal of Pharmacology.
The 'Concise Guide to PHARMACOLOGY', which replaces GRAC, will be a biennial snapshot of succinct overviews of the properties of each target family, intended to be a quick desktop reference guide. Additionally, this will provide a permanent record (DOI: digital object identifier) that will survive database updates and therefore allow the precise context of the database to be understood at any time in the future  (37). Since the Guide to PHARMACOLOGY portal now integrates data from the printed GRAC compendium and IUPHAR-DB, we are planning a phased retirement of IUPHAR-DB. The current URL (http://www. iuphar-db. org) will remain active, with appropriate notices directing users to the Guide to PHARMACOLOGY portal.
DATA ACCESS The Guide to PHARMACOLOGY is available online at http://www. guidetopharmacology. org. The website includes downloadable files containing current receptor and channel lists, NC-IUPHAR nomenclature, synonyms, genetic information, HGNC gene nomenclature and identifiers, and other database accessions. Other file formats are available by emailing enquiries@guidetopharmacology. org. Information on linking to Guide to PHARMACOLOGY pages is provided at http://www. guidetopharmacology. org/linking. jsp. To further facilitate external programmatic and user access to the database, we are developing an application programming interface (API) and Web services. This will allow our content to be exploited in new integration initiatives such as Open PHACTS  (38) , of which we are already an associate member.
ACKNOWLEDGEMENTS The authors thank all the GRAC consultants (a full list of consultants for the Fifth Edition of GRAC can be found at http://onlinelibrary. wiley. com/doi/10. 1111/j. 1476-5381. 2011. 01649_2. x/full). The authors thank all members of  NC-IUPHAR  and its global network of subcommittees for their ongoing support. NC-IUPHAR members:  S. P. H. Alexander ,  T. I. Bonner ,  W. A. Catterall ,  A. Christopoulos ,  A. P. Davenport ,  C. T. Dollery ,  S. Enna ,  D. Fabbro ,  A. J. Harmar ,  K. Kaibuchi ,  Y. Kanai ,  V. Laudet ,  R. R. Neubig ,  E. H. Ohlstein ,  J. A. Peters ,  J. P. Pin ,  U. Ruegg ,  P. du Souich ,  M. Spedding  and  M. W. Wright. The work of  NC-IUPHAR  is supported by the  American Society for Pharmacology and Experimental Therapeutics, Servier ,  GlaxoSmithKline ,  Pfizer ,  Actelion ,  AstraZeneca ,  DiscoveRx ,  Abbott  and  Merck Millipore.
Respiratory infections stemming from influenza viruses and the Severe Acute Respiratory Syndrome corona virus (SARS-CoV) represent a serious public health threat as emerging pandemics. Despite efforts to identify the critical interactions of these viruses with host machinery, the key regulatory events that lead to disease pathology remain poorly targeted with therapeutics. Here we implement an integrated network interrogation approach, in which proteome and transcriptome datasets from infection of both viruses in human lung epithelial cells are utilized to predict regulatory genes involved in the host response. We take advantage of a novel ''crowd-based'' approach to identify and combine ranking metrics that isolate genes/proteins likely related to the pathogenicity of SARS-CoV and influenza virus.
Our group and others have developed advanced bioinformatics capabilities to facilitate the integration of diverse data types  [7, [9] [10] [11]. In this study, we use a network approach to predict critical signaling regulators to influenza virus and severe acute respiratory syndrome coronavirus (SARS-CoV) infection. These respiratory viruses are crucial public health concerns, and the essential mechanisms behind pathogenesis are not well understood. We have generated large-scale time courses of transcriptome and proteome data derived from influenza virus and SARS-CoV infected human bronchial epithelial cells (Calu-3 cells). Our datasets include high and low pathogenicity (HP and LP) strains of both influenza and SARS-CoV viruses, based on published studies in mouse models  [12, 13]. A recent study showed that building a consensus network from multiple inference algorithms yielded a better performing network than any network arising from individual algorithms  [14].
Microarray and proteomic data were collected and processed for all viral strains and conditions as detailed in Materials and Methods. Compendia of all data for each virus type were formed and used for all subsequent analysis. After applying fold change and significance filters (see Methods), SARS-CoV virus transcriptomics were reduced to 8695 differentially expressed (DE) genes (relative to time-matched mocks). For influenza data, we applied the fold change filter to time points at or before 12 hours, since genes affected at later time points were previously shown to be primarily involved in cell death and not regulatory events  [17, 18]. This filtering scheme resulted in 13789 DE genes in influenza virus-infected cells. Proteomic analysis (see Methods) resulted in 859 and 1529 DE proteins for SARS-CoV and influenza viruses, respectively.
The resulting vertices were matched to computed TFtarget pairs from the TRANSFAC dataset available through the molecular signatures database (msigdb, found at http://www. broadinstitute. org/gsea/msigdb/collections. jsp). The inferred transcriptome networks contained significantly more known regulatory edges for SARS-CoV and influenza viruses than seen from randomly scrambled edges (as calculated by z-test, Figure  3a ). As an additional validation of the network inference approach, we utilized an available dataset derived from 400 siRNA knockdown experiments in human umbilical vein endothelial cells (HUVEC)  [19]. We reasoned that network neighbors (or genes with a predicted relationship) should have regulatory influence on one another, such that the set of genes affected by siRNA targeting of a particular gene should be a similar set of genes to those neighboring the target in an association network.
Network Integration Since the proteome data covers only a fraction of the gene products of the transcriptome data platform, the resulting proteome network was much smaller than the transcriptome network, as would be expected. We therefore integrated the two data types into a single network, as demonstrated previously  [7, 11]. We initially merged all of the vertices and edges for the networks, yielding two integrated networks, one for each virus. However, when we ordered all vertices according to their betweenness scores, we identified artificially high betweenness scores for vertices that happen to be common in both datasets, which is unrelated to their true betweenness in the regulatory network (Figure  S1-top ). To alleviate this potential artifact in the integrated networks, we selected proteome edges for integration in which both parent vertices were already present in the transcriptome network (i.
e. , ''conservative integration''; Figure  S2 ). In this way, only new edges (566 for influenza virus, 285 for SARS-CoV), not new vertices, were introduced into the network so that no artificial bottlenecks were introduced (Figure  S1 , bottom). No major changes in degree centrality (hubs) were observed as a result of integration (data not shown). We determined the effect of proteome data integration on network bottlenecks by comparing the difference in betweenness for all vertices before and after integration. We found that proteome data integration had minimal effect on the influenza network, since the top 200 vertices in the integrated network showed only minor shifts in betweenness ranking (less than 30 positions, with 98% less than 5 positions). Further influenza virus network analysis therefore included only transcriptome data.
Msigdb was searched for gene sets with names containing the terms 'virus' or 'viral', which yielded 299 sets. We reasoned that these gene sets would function well for enrichment analysis of general viral processes. We applied the virus-related enrichment analysis to the same SARS-CoV rankings from the integrated network in an identical manner as was applied to the influenza data (Figure  6 ). In contrast to the observations for influenza virus, the DE ranking showed strong enrichment for SARS-CoV, which conferred still higher enrichment on combined rankings that incorporated DE, with the combined degree/ betweenness/DE ranking receiving the highest overall score (Figure  6 ). Comparing virus-related enrichment scores for SARS-CoV rankings and influenza-specific enrichment scores for influenza rankings showed that for both viruses, combining the hub and bottleneck ranking yielded better enrichment than for either ranking alone.
For SARS-CoV, these genes are CREB5, DUSP8, and NFKBIA (Figures S5, S6 and S7 respectively); for influenza virus they are PCGF5, NFE2L3 and HLA-E (Figures S8, S9 and S10 respectively). After establishing the GSEA enrichment ranking approach, we used it to test whether the conservative proteome/transcriptome integration approach described above conferred an improvement over transcriptome networks alone. Betweenness centrality of networks with and without proteome data integration was evaluated. SARS-CoV networks showed a significant enrichment The number of transcriptome network edges for each virus that was also present in the database (red) was compared with the number of matching edges in 1000 random networks (gray) to estimate the number of matching edges expected from chance. B) Relationships between genes targeted in a large siRNA-targeting study  [19]  and the downstream affected genes were compared to relationships predicted from our network inference approach.
Building a Predictive Model to Assess Conservation A critical question regarding the use of model systems is how well the experimental model represents the target system. Calu3 cells provide a convenient in vitro model for lung epithelium, but it is unknown to what extent the pathways and mechanisms found in the in vitro model will translate to a more realistic model, such as primary cells or in vivo animal models. In order to demonstrate that our approach based on in vitro data provides predictive insights relevant to an in vivo model, we used the Inferelator software  [31]  to determine how well models derived from Calu3 data would apply to data from other experimental systems  [18]  (steps 3 and 4 in Figure  2 ). The Inferelator uses multivariate regression to select a small set of regulators that are the most likely to be influencing a regulatory target.
Expression data from the Calu-3 cells were collapsed using hierarchical clustering, such that   clusters of genes with similar expression profiles were chosen as regulatory ''targets''. A list of candidate regulators was supplied to the Inferelator software as input, which was compiled from known transcription factors and the top candidate regulators from our ranking strategy  [32]. A prediction of the critical regulators of a gene cluster can be used to predict the behavior of that gene cluster based on the behavior of its predicted regulators. Behavioral conservation can therefore be evaluated by comparing predicted cluster expression profiles with expression data in a distinct, but comparable biological system. We applied the SARS-CoV model to a dataset of SARS-CoV infection of primary human airway epithelial (HAE) cells.
As an illustration, the expression profile of a single high performing cluster prediction from each virus is included for comparison with the observed expression of the cluster (Figure  7b ). The high performing SARS-CoV clusters were enriched for genes involved in the immune response. The two highest performing influenza clusters were also enriched for immune response genes, while another showed enrichment for transcription regulation. For each virus type, we also generated 100 models from datasets with scrambled genes but with the cluster structure intact. In this way, we compared the observed level of cross-model fitting with the level of model fitting expected by random chance. Figure  7c  shows the average Pearson's correlation of the clusters' predicted profiles and the observed profiles from the new data.
The average correlation of models derived from data with scrambled rows is shown for comparison. While these results demonstrate the high degree of regulatory conservation between the different models, we decided to identify the individual genes that displayed the most conserved response to infection across model systems. To this end, we used the crossprediction metric (Xpred) described in  [18] , which is a measure of how well the behavior of a gene is predicted across model systems. Xpred rankings are shown in Table  S3  (SARS-CoV) and Table  S4  (influenza virus). To determine if these genes showed enrichment in functional categories, we used GO term enrichment analysis on genes with the highest Xpred scores in each of the two virus datasets. For both viruses, genes with a conserved response showed functional enrichment in innate immune response signaling (not shown).
Thus while not all regulatory mechanisms are conserved between model systems, a component of the innate immune response is well-preserved. We next wished to determine the overlap between these genes showing conservation of behavior between model systems, and the genes predicted previously from regulatory candidate ranking (tables 2 and 3). This intersection represents genes from each virus dataset that are both behaviorally conserved across different systems, and are predicted to fill regulatory roles. Figure  2  illustrates the process of isolating these genes. Intersection of the top 5% of each of the two rankings yielded highly significant overlaps, with 37 overlapping genes for SARS (p-value,2e-6, genes in table 4) and 24 for influenza (p-value = 0. 00043, genes in table  5 ). We refer to genes highly ranked in both categories as ''conserved regulators''.
Due to regulation at translational, post-translational and protein stability levels, mRNA levels are not strictly correlated to protein expression and sometimes are not correlated at all. Previous studies demonstrated that integration of proteome data into transcriptome networks improved network performance  [7, 11, 34]. For the current study, we demonstrated an improvement in network performance when SARS-CoV proteome and transcriptome data were integrated. No corresponding improvement was observed from integration of influenza proteome data, which may be related to the fact that unlike the SARS-CoV data sets, not all experimental conditions present in the influenza transcriptome data set had corresponding proteome data. Further, the slower kinetics of SARS-CoV compared to influenza virus may affect what can and can't be observed as protein abundance changes before cell death occurs.