Drug-target interaction prediction with Bipartite Local Models and hubness-aware regression

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F17%3A10360925" target="_blank" >RIV/00216208:11320/17:10360925 - isvavai.cz</a>
Result on the web
<a href="http://www.sciencedirect.com/science/article/pii/S0925231217308111" target="_blank" >http://www.sciencedirect.com/science/article/pii/S0925231217308111</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.neucom.2017.04.055" target="_blank" >10.1016/j.neucom.2017.04.055</a>

Result language
angličtina
Original language name
Drug-target interaction prediction with Bipartite Local Models and hubness-aware regression
Original language description
Computational prediction of drug-target interactions is an essential task with various applications in the pharmaceutical industry, such as adverse effect prediction or drug repositioning. Recently, expert systems based on machine learning have been applied to drug-target interaction prediction. Although hubness-aware machine learning techniques are among the most promising approaches, their potential to enhance drug-target interaction prediction methods has not been exploited yet. In this paper, we extend the Bipartite Local Model (BLM), one of the most prominent interaction prediction methods. In particular, we use BLM with a hubness-aware regression technique, ECkNN. We represent drugs and targets in the similarity space with rich set of features (i.e., chemical, genomic and interaction features), and build a projection-based ensemble of BLMs. In order to assist reproducibility of our work as well as comparison to published results, we perform experiments on widely used publicly available drug-target interaction datasets. The results show that our approach outperforms state-of-the-art drug-target prediction techniques. Additionally, we demonstrate the feasibility of predictions from the point of view of applications.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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