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.
An improved conflicting-evidence combination method based on the redistribution of the basic probability assignment