Modified linear regression predicts drug-target interactions accurately

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10416950" target="_blank" >RIV/00216208:11320/20:10416950 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=jIkSBxUjb" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=jIkSBxUjb</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1371/journal.pone.0230726" target="_blank" >10.1371/journal.pone.0230726</a>

Result language
angličtina
Original language name
Modified linear regression predicts drug-target interactions accurately
Original language description
State-of-the-art approaches for the prediction of drug-target interactions (DTI) are based on various techniques, such as matrix factorisation, restricted Boltzmann machines, network-based inference and bipartite local models (BLM). In this paper, we propose the framework of Asymmetric Loss Models (ALM) which is more consistent with the underlying chemical reality compared with conventional regression techniques. Furthermore, we propose to use an asymmetric loss model with BLM to predict drug-target interactions accurately. We evaluate our approach on publicly available real-world drug-target interaction datasets. The results show that our approach outperforms state-of-the-art DTI techniques, including recent versions of BLM.
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
—
OECD FORD branch
10600 - Biological sciences

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

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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