Computational drug repurposing aims at finding new medical uses for
existing drugs. The identification of novel drug-target interactions (DTIs) can be a useful part of such a task. Computational determination of DTIs is a convenient
strategy for systematic screening of a large number of drugs in the attempt to
identify new DTIs at low cost and with reasonable accuracy. This necessitates
development of accurate computational methods that can help focus on the
follow-up experimental validation on a smaller number of highly likely targets for
a drug. Although many methods have been proposed for computational DTI
prediction, they suffer the high false positive prediction rate or they do not predict the effect that drugs exert on targets in DTIs.
In this report, first, we present a comprehensive review of the recent progress in
the field of DTI prediction from data-centric and algorithm-centric perspectives.
The aim is to provide a comprehensive review of computational methods for
identifying DTIs, which could help in constructing more reliable methods. Then,
we present DDR, an efficient method to predict the existence of DTIs. DDR
achieves significantly more accurate results compared to the other state-of-theart methods. As supported by independent evidences, we verified as correct 22 out of the top 25 DDR DTIs predictions. This validation proves the practical utility of DDR, suggesting that DDR can be used as an efficient method to identify
5 correct DTIs. Finally, we present DDR-FE method that predicts the effect types of a drug on its target. On different representative datasets, under various test
setups, and using different performance measures, we show that DDR-FE
achieves extremely good performance. Using blind test data, we verified as
correct 2,300 out of 3,076 DTIs effects predicted by DDR-FE. This suggests that DDR-FE can be used as an efficient method to identify correct effects of a drug on its target.
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/626424 |
| Date | 12 1900 |
| Creators | Olayan, Rawan S. |
| Contributors | Bajic, Vladimir B., Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Moshkov, Mikhail, Laleg-Kirati, Taous-Meriem, Christoffels, Alan |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Rights | 2018-12-25, At the time of archiving, the student author of this dissertation opted to temporarily restrict access to it. The full text of this dissertation became available to the public after the expiration of the embargo on 2018-12-25. |
Page generated in 0.0155 seconds