Emig, D., Ivliev, A., Pustovalova, O., Lancashire, L., Bureeva, S., Nikolsky, Y. and Bessarabova, M. (2013) Drug Target Prediction and Repositioning Using an Integrated Network-Based Approach. PLoS ONE, 8, e60618.
http://dx.doi.org/10.1371/journal.pone.0060618

has been cited by the following article:

• TITLE: Application of Word Embedding to Drug Repositioning

  AUTHORS: Duc Luu Ngo, Naoki Yamamoto, Vu Anh Tran, Ngoc Giang Nguyen, Dau Phan, Favorisen Rosyking Lumbanraja, Mamoru Kubo, Kenji Satou

  KEYWORDS: Distributed Representation of Word Sense, Discovery of Drug-Disease Relation, Word Analogy

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.9 No.1, January 21, 2016

  ABSTRACT: As a key technology of rapid and low-cost drug development, drug repositioning is getting popular. In this study, a text mining approach to the discovery of unknown drug-disease relation was tested. Using a word embedding algorithm, senses of over 1.7 million words were well represented in sufficiently short feature vectors. Through various analysis including clustering and classification, feasibility of our approach was tested. Finally, our trained classification model achieved 87.6% accuracy in the prediction of drug-disease relation in cancer treatment and succeeded in discovering novel drug-disease relations that were actually reported in recent studies.