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2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2015)
Washington, DC, USA
Nov. 9, 2015 to Nov. 12, 2015
ISBN: 978-1-4673-6798-1
pp: 669-674
Majid Rastegar-Mojarad , Department of Health Sciences Research, Mayo Clinic, USA
Ravikumar Komandur Elayavilli , Department of Health Sciences Research, Mayo Clinic, USA
Dingcheng Li , Department of Health Sciences Research, Mayo Clinic, USA
Rashmi Prasad , University of Wisconsin-Milwaukee, USA
Hongfang Liu , Department of Health Sciences Research, Mayo Clinic, USA
ABSTRACT
Drug repositioning has been a topic of great attention to researchers and pharmaceutical companies due to its significant impact on the cost of drug discovery. There are several approaches to identify potentially novel drug candidates through repurposing. Literature mining has played a critical role in mining such information from scientific articles. In this paper, we used drug-gene and gene-disease semantic predications extracted from Medline abstracts to generate a list of potential drug-disease pairs. We further ranked the generated pairs, by assigning scores based on the predicates that qualify drug-gene and gene-disease relationships. On comparing the top-ranked drug-disease pairs against the Comparative Toxicogenomics Database (CTD), a curated database for drug-disease relations, we found that a significant percentage of top ranked pairs appeared in CTD. Co-occurrence of these high-ranked pairs in Medline abstracts further improves the confidence in our approach to rank the inferred drug-disease relations higher in the list. Finally, manual evaluation of top ten pairs ranked by our approach revealed that nine of them have some biological significance based on expert judgment.
INDEX TERMS
Semantic Predication, Drug repositioning, Literature-based discovery
CITATION

M. Rastegar-Mojarad, R. K. Elayavilli, D. Li, R. Prasad and H. Liu, "A new method for prioritizing drug repositioning candidates extracted by literature-based discovery," 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Washington, DC, USA, 2015, pp. 669-674.
doi:10.1109/BIBM.2015.7359766
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