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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Neurocomputing

ISSN

0925-2312

e-ISSN

—

Svazek periodika

260

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

10

Strana od-do

284-293

Kód UT WoS článku

000405536900030

EID výsledku v databázi Scopus

2-s2.0-85019889766