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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jian-Yu Shi Siu-Ming Yiu Yiming Li Leung, H.C.M. Chin, F.Y.L. |
| Copyright Year | 2014 |
| Description | Author affiliation: Dept. of Psychiatry, Univ. of Hong Kong, Hong Kong, China (Yiming Li) || Dept. of Comput. Sci., Univ. of Hong Kong, Hong Kong, China (Siu-Ming Yiu; Leung, H.C.M.; Chin, F.Y.L.) || Sch. of Life Sci., Northwestern Polytech. Univ., Xi'an, China (Jian-Yu Shi) |
| Abstract | Predicting drug-target interaction using computational approaches is an important step in drug discovery and repositioning. To predict whether there will be an interaction between a drug and a target, most existing methods identify similar drugs and targets in the database. The prediction is then made based on the known interactions of these drugs and targets. This idea is promising. However, there are two shortcomings that have not yet been addressed appropriately. Firstly, most of the methods only use 2D chemical structures and protein sequences to measure the similarity of drugs and targets respectively. However, this information may not fully capture the characteristics determining whether a drug will interact with a target. Secondly, there are very few known interactions, i.e. many interactions are “missing” in the database. Existing approaches are biased towards known interactions and have no good solutions to handle possibly missing interactions which affect the accuracy of the prediction. In this paper, we enhance the similarity measures to include non-structural (and non-sequence-based) information and introduce the concept of a “super-target” to handle the problem of possibly missing interactions. Based on evaluations on real data, we show that our similarity measure is better than the existing measures and our approach is able to achieve higher accuracy than the two best existing algorithms, WNN-GIP and KBMF2K. |
| Starting Page | 45 |
| Ending Page | 50 |
| File Size | 343389 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479956692 |
| DOI | 10.1109/BIBM.2014.6999125 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-11-02 |
| Publisher Place | UK |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Drugs Proteins Databases Semantics Prediction algorithms Chemicals Diffusion tensor imaging |
| Content Type | Text |
| Resource Type | Article |
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