The Biological General Repository for Interaction Datasets (BioGRID) is a public database that archives and disseminates genetic and protein
 interaction data from model organisms and humans
 (http://www.thebiogrid.org). BioGRID currently holds 347 966
 interactions (170 162 genetic, 177 804 protein) curated from both
 high-throughput data sets and individual focused studies, as derived
 from over 23 000 publications in the primary literature. Complete
 coverage of the entire literature is maintained for budding yeast
 (Saccharomyces cerevisiae), fission yeast (Schizosaccharomyces pombe)
 and thale cress (Arabidopsis thaliana), and efforts to expand curation
 across multiple metazoan species are underway. The BioGRID houses 48
 831 human protein interactions that have been curated from 10 247
 publications. Current curation drives are focused on particular areas
 of biology to enable insights into conserved networks and pathways that
 are relevant to human health. The BioGRID 3.0 web interface contains
 new search and display features that enable rapid queries across
 multiple data types and sources. An automated Interaction Management
 System (IMS) is used to prioritize, coordinate and track curation
 across international sites and projects. BioGRID provides interaction
 data to several model organism databases, resources such as Entrez-Gene
 and other interaction meta-databases. The entire BioGRID 3.0 data
 collection may be downloaded in multiple file formats, including PSI MI
 XML. Source code for BioGRID 3.0 is freely available without any
 restrictions.

/pdf/the-biogrid-interaction-database-2011-update-4t23jtqdsl.pdf

The BioGRID Interaction Database: 2011 update

The Biological General Repository for Interaction Datasets (BioGRID: http//thebiogrid.org) is an open access archive of genetic and protein interactions that are curated from the primary biomedical literature for all major model organism species. As of September 2012, BioGRID houses more than 500 000 manually annotated interactions from more than 30 model organisms. BioGRID maintains complete curation coverage of the literature for the budding yeast Saccharomyces cerevisiae, the fission yeast Schizosaccharomyces pombe and the model plant Arabidopsis thaliana. A number of themed curation projects in areas of biomedical importance are also supported. BioGRID has established collaborations and/or shares data records for the annotation of interactions and phenotypes with most major model organism databases, including Saccharomyces Genome Database, PomBase, WormBase, FlyBase and The Arabidopsis Information Resource. BioGRID also actively engages with the text-mining community to benchmark and deploy automated tools to expedite curation workflows. BioGRID data are freely accessible through both a user-defined interactive interface and in batch downloads in a wide variety of formats, including PSI-MI2.5 and tab-delimited files. BioGRID records can also be interrogated and analyzed with a series of new bioinformatics tools, which include a post-translational modification viewer, a graphical viewer, a REST service and a Cytoscape plugin.

https://www.pure.ed.ac.uk/ws/files/8144915/NAR_2013_Tyres_M.pdf

The BioGRID interaction database: 2013 update

The Biological General Repository for Interaction Datasets (BioGRID: http://thebiogrid.org) is an open access database that houses genetic and protein interactions curated from the primary biomedical literature for all major model organism species and humans. As of September 2014, the BioGRID contains 749 912 interactions as drawn from 43 149 publications that represent 30 model organisms. This interaction count represents a 50% increase compared to our previous 2013 BioGRID update. BioGRID data are freely distributed through partner model organism databases and meta-databases and are directly downloadable in a variety of formats. In addition to general curation of the published literature for the major model species, BioGRID undertakes themed curation projects in areas of particular relevance for biomedical sciences, such as the ubiquitin-proteasome system and various human disease-associated interaction networks. BioGRID curation is coordinated through an Interaction Management System (IMS) that facilitates the compilation interaction records through structured evidence codes, phenotype ontologies, and gene annotation. The BioGRID architecture has been improved in order to support a broader range of interaction and post-translational modification types, to allow the representation of more complex multi-gene/protein interactions, to account for cellular phenotypes through structured ontologies, to expedite curation through semi-automated text-mining approaches, and to enhance curation quality control.

/pdf/the-biogrid-interaction-database-2015-update-nqn27vvq8w.pdf

The BioGRID interaction database: 2015 update

The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) provides information about interactions between environmental chemicals and gene products and their relationships to diseases. Chemical-gene, chemical-disease and gene-disease interactions manually curated from the literature are integrated to generate expanded networks and predict many novel associations between different data types. CTD now contains over 15 million toxicogenomic relationships. To navigate this sea of data, we added several new features, including DiseaseComps (which finds comparable diseases that share toxicogenomic profiles), statistical scoring for inferred gene-disease and pathway-chemical relationships, filtering options for several tools to refine user analysis and our new Gene Set Enricher (which provides biological annotations that are enriched for gene sets). To improve data visualization, we added a Cytoscape Web view to our ChemComps feature, included color-coded interactions and created a 'slim list' for our MEDIC disease vocabulary (allowing diseases to be grouped for meta-analysis, visualization and better data management). CTD continues to promote interoperability with external databases by providing content and cross-links to their sites. Together, this wealth of expanded chemical-gene-disease data, combined with novel ways to analyze and view content, continues to help users generate testable hypotheses about the molecular mechanisms of environmental diseases.

The Comparative Toxicogenomics Database: update 2013

In recent years, even with remarkable scientific advancements and a significant increase of global research and development spending, drugs are frequently withdrawn from markets. This is primarily due to their side effects or toxicities. Drug molecules often interact with multiple targets, coined as polypharmacology, and the unintended drug-target interactions could cause side effects. Polypharmacology remains one of the major challenges in drug development, and it opens novel avenues to rationally design the next generation of more effective, but less toxic, therapeutic agents. This review outlines the latest progress and challenges in polypharmacology studies.

/pdf/polypharmacology-drug-discovery-for-the-future-102ow57xw5.pdf

Polypharmacology: drug discovery for the future.

Molecular biology has become heavily dependent on biological knowledge encoded in expert curated biological databases. As the volume of biological literature increases, biocurators need help in keeping up with the literature; (semi-) automated aids for biocuration would seem to be an ideal application for natural language processing and text mining. However, to date, there have been few documented successes for improving biocuration throughput using text mining. Our initial investigations took place for the workshop on ‘Text Mining for the BioCuration Workflow’ at the third International Biocuration Conference (Berlin, 2009). We interviewed biocurators to obtain workflows from eight biological databases. This initial study revealed high-level commonalities, including (i) selection of documents for curation; (ii) indexing of documents with biologically relevant entities (e.g. genes); and (iii) detailed curation of specific relations (e.g. interactions); however, the detailed workflows also showed many variabilities. Following the workshop, we conducted a survey of biocurators. The survey identified biocurator priorities, including the handling of full text indexed with biological entities and support for the identification and prioritization of documents for curation. It also indicated that two-thirds of the biocuration teams had experimented with text mining and almost half were using text mining at that time. Analysis of our interviews and survey provide a set of requirements for the integration of text mining into the biocuration workflow. These can guide the identification of common needs across curated databases and encourage joint experimentation involving biocurators, text mining developers and the larger biomedical research community.

https://repositorium.sdum.uminho.pt/bitstream/1822/23460/1/bas020.full.pdf

Text mining for the biocuration workflow

Molecular biology has become heavily dependent on biological knowledge encoded in expert curated biological databases.As the volume of biological literature increases, biocurators need help in keeping up with the literature; (semi-) automatedaids for biocuration would seem to be an ideal application for natural language processing and text mining. However, todate, there have been few documented successes for improving biocuration throughput using text mining. Our initialinvestigations took place for the workshop on ‘Text Mining for the BioCuration Workflow’ at the third InternationalBiocuration Conference (Berlin, 2009). We interviewed biocurators to obtain workflows from eight biological databases.This initial study revealed high-level commonalities, including (i) selection of documents for curation; (ii) indexing ofdocuments with biologically relevant entities (e.g. genes); and (iii) detailed curation of specific relations (e.g. interactions);however, the detailed workflows also showed many variabilities. Following the workshop, we conducted a survey ofbiocurators. The survey identified biocurator priorities, including the handling of full text indexed with biological entitiesand support for the identification and prioritization of documents for curation. It also indicated that two-thirds ofthe biocuration teams had experimented with text mining and almost half were using text mining at that time. Analysisof our interviews and survey provide a set of requirements for the integration of text mining into the biocuration work-flow. These can guide the identification of common needs across curated databases and encourage joint experimentationinvolving biocurators, text mining developers and the larger biomedical research community.

Original article Text mining for the biocuration workflow

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Text mining for the biocuration workflow

Original article Text mining for the biocuration workflow