A Drug Target Interaction Prediction Based on LINE-RF Learning

Title:A Drug Target Interaction Prediction Based on LINE-RF Learning

Volume: 15 Issue: 7

Author(s): Jihong Wang, Yue Shi, Xiaodan Wang and Huiyou Chang*

Affiliation:

• School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, Guangdong,China

Keywords: Drug-target interactions, network representation learning, LINE, random forests, DrugBank, topology.

Abstract:

Background: At present, using computer methods to predict drug-target interactions (DTIs) is a very important step in the discovery of new drugs and drug relocation processes. The potential DTIs identified by machine learning methods can provide guidance in biochemical or clinical experiments.

Objective: The goal of this article is to combine the latest network representation learning methods for drug-target prediction research, improve model prediction capabilities, and promote new drug development.

Methods: We use large-scale information network embedding (LINE) method to extract network topology features of drugs, targets, diseases, etc., integrate features obtained from heterogeneous networks, construct binary classification samples, and use random forest (RF) method to predict DTIs.

Results: The experiments in this paper compare the common classifiers of RF, LR, and SVM, as well as the typical network representation learning methods of LINE, Node2Vec, and DeepWalk. It can be seen that the combined method LINE-RF achieves the best results, reaching an AUC of 0.9349 and an AUPR of 0.9016.

Conclusion: The learning method based on LINE network can effectively learn drugs, targets, diseases and other hidden features from the network topology. The combination of features learned through multiple networks can enhance the expression ability. RF is an effective method of supervised learning. Therefore, the Line-RF combination method is a widely applicable method.

Cite this article as:

Wang Jihong , Shi Yue , Wang Xiaodan and Chang Huiyou *, A Drug Target Interaction Prediction Based on LINE-RF Learning, Current Bioinformatics 2020; 15 (7) . https://dx.doi.org/10.2174/1574893615666191227092453