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.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 669-674 |
| Number of pages | 6 |
| ISBN (Print) | 9781467367981 |
| DOIs | |
| State | Published - Dec 16 2015 |
| Event | IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 - Washington, United States Duration: Nov 9 2015 → Nov 12 2015 |
Other
| Other | IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 |
|---|---|
| Country | United States |
| City | Washington |
| Period | 11/9/15 → 11/12/15 |
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Keywords
- Drug repositioning
- Literature-based discovery
- Semantic Predication
ASJC Scopus subject areas
- Software
- Artificial Intelligence
- Health Informatics
- Biomedical Engineering
Cite this
A new method for prioritizing drug repositioning candidates extracted by literature-based discovery. / Rastegar-Mojarad, Majid; Elayavilli, Ravikumar Komandur; Li, Dingcheng; Prasad, Rashmi; Liu, Hongfang D.
Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 669-674 7359766.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A new method for prioritizing drug repositioning candidates extracted by literature-based discovery
AU - Rastegar-Mojarad, Majid
AU - Elayavilli, Ravikumar Komandur
AU - Li, Dingcheng
AU - Prasad, Rashmi
AU - Liu, Hongfang D
PY - 2015/12/16
Y1 - 2015/12/16
N2 - 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.
AB - 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.
KW - Drug repositioning
KW - Literature-based discovery
KW - Semantic Predication
UR - http://www.scopus.com/inward/record.url?scp=84962425614&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84962425614&partnerID=8YFLogxK
U2 - 10.1109/BIBM.2015.7359766
DO - 10.1109/BIBM.2015.7359766
M3 - Conference contribution
AN - SCOPUS:84962425614
SN - 9781467367981
SP - 669
EP - 674
BT - Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
PB - Institute of Electrical and Electronics Engineers Inc.
ER -