Extraction of drug-drug interaction using neural embedding

Wen Juan Hou, Bamfa Ceesay

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Information on changes in a drug's effect when taken in combination with a second drug, known as drug-drug interaction (DDI), is relevant in the pharmaceutical industry. DDIs can delay, decrease, or enhance absorption of either drug and thus decrease or increase their action or cause adverse effects. Information Extraction (IE) can be of great benefit in allowing identification and extraction of relevant information on DDIs. We here propose an approach for the extraction of DDI from text using neural word embedding to train a machine learning system. Results show that our system is competitive against other systems for the task of extracting DDIs, and that significant improvements can be achieved by learning from word features and using a deep-learning approach. Our study demonstrates that machine learning techniques such as neural networks and deep learning methods can efficiently aid in IE from text. Our proposed approach is well suited to play a significant role in future research.

Original languageEnglish
Article number18400279
JournalJournal of Bioinformatics and Computational Biology
Volume16
Issue number6
DOIs
Publication statusPublished - 2018 Dec 1

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Drug interactions
Drug Interactions
Information Storage and Retrieval
Learning systems
Pharmaceutical Preparations
Learning
Drug products
Drug Industry
Neural networks
Industry
Deep learning

Keywords

  • data abstraction
  • Drug-drug interaction
  • long short term memory (LSTM)
  • neural networks
  • word embedding

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computer Science Applications

Cite this

Extraction of drug-drug interaction using neural embedding. / Hou, Wen Juan; Ceesay, Bamfa.

In: Journal of Bioinformatics and Computational Biology, Vol. 16, No. 6, 18400279, 01.12.2018.

Research output: Contribution to journalArticle

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