ALADIN: A New Approach for Drug--Target Interaction Prediction
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
ALADIN: A New Approach for Drug--Target Interaction Prediction
Original language description
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.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Machine Learning and Knowledge Discovery in Databases
ISBN
978-3-319-71246-8
ISSN
—
e-ISSN
neuvedeno
Number of pages
16
Pages from-to
322-337
Publisher name
Springer International Publishing
Place of publication
Cham
Event location
SKOPJE, MACEDONIA
Event date
Sep 18, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—