EmDL : Extracting miRNA-Drug Interactions from Literature

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

13 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Article number3370679
Pages (from-to)1722-1728
Journal / PublicationIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume16
Issue number5
Online published6 Jul 2017
Publication statusPublished - Sept 2019

Abstract

The microRNAs (miRNAs), regulators of post-transcriptional processes, have been found to affect the efficacy of drugs by regulating the biological processes in which the target proteins of drugs may be involved. For example, some drugs develop resistance when certain miRNAs are overexpressed. Therefore, identifying miRNAs that affect drug effects can help understand the mechanisms of drug actions and design more efficient drugs. Although some computational approaches have been developed to predict miRNA-drug associations, such associations rarely provide explicit information about which miRNAs and how they affect drug efficacy. On the other hand, there are rich information about which miRNAs affect the efficacy of which drugs in the literature. In this paper, we present a novel text mining approach, named as EmDL (Extracting miRNA-Drug interactions from Literature), to extract the relationships of miRNAs affecting drug efficacy from literature. Benchmarking on the drug-miRNA interactions manually extracted from MEDLINE and PubMed Central, EmDL outperforms traditional text mining approaches as well as other popular methods for predicting drug-miRNA associations. Specifically, EmDL can effectively identify the sentences that describe the relationships of miRNAs affecting drug effects. The drug-miRNA interactome presented here can help understand how miRNAs affect drug effects and provide insights into the mechanisms of drug actions. In addition, with the information about drug-miRNA interactions, more effective drugs or combinatorial strategies can be designed in the future. The data used here can be accessed at http://mtd.comp-sysbio.org/.

Research Area(s)

  • Databases, drug efficacy, Drugs, event extraction, Feature extraction, Immune system, microRNA, Text mining

Citation Format(s)

EmDL: Extracting miRNA-Drug Interactions from Literature. / Xie, Wen-Bin; Yan, Hong; Zhao, Xing-Ming.
In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 16, No. 5, 3370679, 09.2019, p. 1722-1728.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review