Literature DB >> 22238258

IMID: integrated molecular interaction database.

Sentil Balaji1, Charles Mcclendon, Rajesh Chowdhary, Jun S Liu, Jinfeng Zhang.   

Abstract

MOTIVATION: Molecular interaction information, such as protein-protein interactions and protein-small molecule interactions, is indispensable for understanding the mechanism of biological processes and discovering treatments for diseases. Many databases have been built by manual annotation of literature to organize such information into structured form. However, most databases focus on only one type of interactions, which are often not well annotated and integrated with related functional information.
RESULTS: In this study, we integrate molecular interaction information from literature by automatic information extraction and from manually annotated databases. We further integrate the relationships between protein/gene and other bio-entity terms including gene ontology terms, pathways, species and diseases to build an integrated molecular interaction database (IMID). Interactions can be selected by their associated probabilities. IMID allows complex and versatile queries for context-specific molecular interactions, which are not available currently in other molecular interaction databases. AVAILABILITY: The database is located at www.integrativebiology.org.

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Year:  2012        PMID: 22238258      PMCID: PMC3289914          DOI: 10.1093/bioinformatics/bts010

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  34 in total

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3.  The Database of Interacting Proteins: 2004 update.

Authors:  Lukasz Salwinski; Christopher S Miller; Adam J Smith; Frank K Pettit; James U Bowie; David Eisenberg
Journal:  Nucleic Acids Res       Date:  2004-01-01       Impact factor: 16.971

4.  Knowledge discovery by automated identification and ranking of implicit relationships.

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5.  Manual curation is not sufficient for annotation of genomic databases.

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7.  Large-scale directional relationship extraction and resolution.

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2.  Identification of disease treatment mechanisms through the multiscale interactome.

Authors:  Camilo Ruiz; Marinka Zitnik; Jure Leskovec
Journal:  Nat Commun       Date:  2021-03-19       Impact factor: 14.919

3.  Extracting chemical-protein interactions from literature using sentence structure analysis and feature engineering.

Authors:  Pei-Yau Lung; Zhe He; Tingting Zhao; Disa Yu; Jinfeng Zhang
Journal:  Database (Oxford)       Date:  2019-01-01       Impact factor: 3.451

4.  Triage of documents containing protein interactions affected by mutations using an NLP based machine learning approach.

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5.  Pre-trained models, data augmentation, and ensemble learning for biomedical information extraction and document classification.

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6.  STRING v9.1: protein-protein interaction networks, with increased coverage and integration.

Authors:  Andrea Franceschini; Damian Szklarczyk; Sune Frankild; Michael Kuhn; Milan Simonovic; Alexander Roth; Jianyi Lin; Pablo Minguez; Peer Bork; Christian von Mering; Lars J Jensen
Journal:  Nucleic Acids Res       Date:  2012-11-29       Impact factor: 16.971

  6 in total

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