METSP: A Maximum-Entropy Classifier Based Text Mining Tool for Transporter-Substrate Identification with Semistructured Text

Min Zhao; Yanming Chen; Dacheng Qu; Hong Qu

doi:10.1155/2015/254838

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METSP: A Maximum-Entropy Classifier Based Text Mining Tool for Transporter-Substrate Identification with Semistructured Text

Journal article

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METSP: A Maximum-Entropy Classifier Based Text Mining Tool for Transporter-Substrate Identification with Semistructured Text

Min Zhao, Yanming Chen, Dacheng Qu and Hong Qu

BioMed Research International, Vol.2015, 254838

2015

DOI: https://doi.org/10.1155/2015/254838

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Abstract

METSP

The substrates of a transporter are not only useful for inferring function of the transporter, but also important to discover compound-compound interaction and to reconstruct metabolic pathway. Though plenty of data has been accumulated with the developing of new technologies such as in vitro transporter assays, the search for substrates of transporters is far from complete. In this article, we introduce METSP, a maximum-entropy classifier devoted to retrieve transporter-substrate pairs (TSPs) from semistructured text. Based on the high quality annotation from UniProt, METSP achieves high precision and recall in cross-validation experiments. When METSP is applied to 182,829 human transporter annotation sentences in UniProt, it identifies 3942 sentences with transporter and compound information. Finally, 1547 confidential human TSPs are identified for further manual curation, among which 58.37% pairs with novel substrates not annotated in public transporter databases. METSP is the first efficient tool to extract TSPs from semistructured annotation text in UniProt. This tool can help to determine the precise substrates and drugs of transporters, thus facilitating drug-target prediction, metabolic network reconstruction, and literature classification.

Details

Title: METSP: A Maximum-Entropy Classifier Based Text Mining Tool for Transporter-Substrate Identification with Semistructured Text
Authors: Min Zhao (Author) - University of the Sunshine Coast - Faculty of Science, Health, Education and Engineering
Yanming Chen (Author) - Beijing Institute of Technology, China
Dacheng Qu (Author) - Beijing Institute of Technology, China
Hong Qu (Author) - Peking University, China
Publication details: BioMed Research International, Vol.2015, 254838; 7
Publisher: Hindawi Publishing Corporation
Date published: 2015
DOI: 10.1155/2015/254838
ISSN: 2314-6133
Copyright note: Copyright © 2015 Min Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Organisation Unit: School of Science and Engineering - Legacy; University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Centre for Bioinnovation
Language: English
Record Identifier: 99449334402621
Output Type: Journal article

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Collaboration types: Domestic collaboration; International collaboration
Web Of Science research areas: Biotechnology & Applied Microbiology; Medicine, Research & Experimental

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