ALADIN: A New Approach for Drug--Target Interaction Prediction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F17%3A10360926" target="_blank" >RIV/00216208:11320/17:10360926 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-71246-8_20" target="_blank" >http://dx.doi.org/10.1007/978-3-319-71246-8_20</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-71246-8_20" target="_blank" >10.1007/978-3-319-71246-8_20</a>

Jazyk výsledku

angličtina

Název v původním jazyce

ALADIN: A New Approach for Drug--Target Interaction Prediction

Popis výsledku v původním jazyce

Due to its pharmaceutical applications, one of the most prominent machine learning challenges in bioinformatics is the prediction of drug-target interactions. State-of-the-art approaches are based on various techniques, such as matrix factorization, restricted Boltzmann machines, network-based inference and bipartite local models (BLM). In this paper, we extend BLM by the incorporation of a hubness-aware regression technique coupled with an enhanced representation of drugs and targets in a multi-modal similarity space. Additionally, we propose to build a projection-based ensemble. Our Advanced Local Drug-Target Interaction Prediction technique (ALADIN) is evaluated on publicly available realworld drug-target interaction datasets. The results show that our approach statistically significantly outperforms BLM-NII, a recent version of BLM, as well as NetLapRLS and WNN-GIP.

Název v anglickém jazyce

ALADIN: A New Approach for Drug--Target Interaction Prediction

Popis výsledku anglicky

Due to its pharmaceutical applications, one of the most prominent machine learning challenges in bioinformatics is the prediction of drug-target interactions. State-of-the-art approaches are based on various techniques, such as matrix factorization, restricted Boltzmann machines, network-based inference and bipartite local models (BLM). In this paper, we extend BLM by the incorporation of a hubness-aware regression technique coupled with an enhanced representation of drugs and targets in a multi-modal similarity space. Additionally, we propose to build a projection-based ensemble. Our Advanced Local Drug-Target Interaction Prediction technique (ALADIN) is evaluated on publicly available realworld drug-target interaction datasets. The results show that our approach statistically significantly outperforms BLM-NII, a recent version of BLM, as well as NetLapRLS and WNN-GIP.

Druh

D - Stať ve sborníku

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 statě ve sborníku

Machine Learning and Knowledge Discovery in Databases

ISBN

978-3-319-71246-8

ISSN

—

e-ISSN

neuvedeno

Počet stran výsledku

16

Strana od-do

322-337

Název nakladatele

Springer International Publishing

Místo vydání

Cham

Místo konání akce

SKOPJE, MACEDONIA

Datum konání akce

18. 9. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—