Journal Article

Drug-target interaction prediction with tree-ensemble learning and output space reconstruction

Vens, Celine, Pliakos, Konstantinos

2020

Overview

Background Computational prediction of drug-target interactions (DTI) is vital for drug discovery. The experimental identification of interactions between drugs and target proteins is very onerous. Modern technologies have mitigated the problem, leveraging the development of new drugs. However, drug development remains extremely expensive and time consuming. Therefore, in silico DTI predictions based on machine learning can alleviate the burdensome task of drug development. Many machine learning approaches have been proposed over the years for DTI prediction. Nevertheless, prediction accuracy and efficiency are persisting problems that still need to be tackled. Here, we propose a new learning method which addresses DTI prediction as a multi-output prediction task by learning ensembles of multi-output bi-clustering trees (eBICT) on reconstructed networks. In our setting, the nodes of a DTI network (drugs and proteins) are represented by features (background information). The interactions between the nodes of a DTI network are modeled as an interaction matrix and compose the output space in our problem. The proposed approach integrates background information from both drug and target protein spaces into the same global network framework. Results We performed an empirical evaluation, comparing the proposed approach to state of the art DTI prediction methods and demonstrated the effectiveness of the proposed approach in different prediction settings. For evaluation purposes, we used several benchmark datasets that represent drug-protein networks. We show that output space reconstruction can boost the predictive performance of tree-ensemble learning methods, yielding more accurate DTI predictions. Conclusions We proposed a new DTI prediction method where bi-clustering trees are built on reconstructed networks. Building tree-ensemble learning models with output space reconstruction leads to superior prediction results, while preserving the advantages of tree-ensembles, such as scalability, interpretability and inductive setting.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Algorithms / Analysis / Benchmarking / Bioinformatics / Biomedical and Life Sciences / Building renovation / Classification / Cluster Analysis / Clustering / Computational Biology/Bioinformatics / Computer Appl. in Life Sciences / Computer applications / Computer Simulation / Disease / Drug Development / Drug discovery / Drug Discovery - methods / Drug interactions / Drug-target networks / Drugs / Ensemble learning / Interaction prediction / Learning algorithms / Life Sciences / Ligands / Machine Learning / Machine Learning and Artificial Intelligence in Bioinformatics / Methodology / Methodology Article / Microarrays / multi-output prediction / Network reconstruction / Networks / Nodes / Noise / Performance prediction / Production management / Proteins / Proteins - drug effects / Reconstruction / Setting (Literature) / Teaching methods / Technology / Therapeutic targets / Time / Tree-ensembles / Trees