DTi2Vec: Drug-target interaction prediction using network embedding and ensemble learning.

ABSTRACT: Drug-target interaction (DTI) prediction is a crucial step in drug discovery and repositioning as it reduces experimental validation costs if done right. Thus, developing in-silico methods to predict potential DTI has become a competitive research niche, with one of its main focuses being improving the prediction accuracy. Using machine learning (ML) models for this task, specifically network-based approaches, is effective and has shown great advantages over the other computational methods. However, ML model development involves upstream hand-crafted feature extraction and other processes that impact prediction accuracy. Thus, network-based representation learning techniques that provide automated feature extraction combined with traditional ML classifiers dealing with downstream link pred

SUBMITTER: Thafar MA 

PROVIDER: S-EPMC8459562 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature