Integrating multi-scale neighbouring topologies and cross-modal similarities for drug-protein interaction prediction.

Journal: Briefings in bioinformatics

Published Date: Sep 2, 2021

Abstract

MOTIVATION: Identifying the proteins that interact with drugs can reduce the cost and time of drug development. Existing computerized methods focus on integrating drug-related and protein-related data from multiple sources to predict candidate drug-target interactions (DTIs). However, multi-scale neighboring node sequences and various kinds of drug and protein similarities are neither fully explored nor considered in decision making.

Authors

Ping Xuan

School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China.
Yu Zhang

College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.
Hui Cui

Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, 1278 Keyuan Road, Shanghai 201203, PR China; School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, PR China.
Tiangang Zhang

School of Mathematical Science, Heilongjiang University, Harbin 150080, China. zhang@hlju.edu.cn.
Maozu Guo

School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China.
Toshiya Nakaguchi

Keywords

Algorithms Computational Biology Drug Development Humans Machine Learning Models, Theoretical Pharmaceutical Preparations Protein Binding Proteins Reproducibility of Results Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (33839743)

Integrating multi-scale neighbouring topologies and cross-modal similarities for drug-protein interaction prediction.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals