DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features

• Published in: Briefings in bioinformatics Vol. 22; no. 1; pp. 451 - 462
• Main Authors: Chu, Yanyi, Kaushik, Aman Chandra, Wang, Xiangeng, Wang, Wei, Zhang, Yufang, Shan, Xiaoqi, Salahub, Dennis Russell, Xiong, Yi, Wei, Dong-Qing
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 18.01.2021; Oxford Publishing Limited (England)
• Subjects: Artificial neural networks; Computer applications; Datasets; Drugs; Feature extraction; Machine learning; Neural networks; Predictions; Similarity; Source code; Therapeutic targets; ensemble learning; drug-target interaction; machine learning; cascade deep forest

Abstract Drug–target interactions (DTIs) play a crucial role in target-based drug discovery and development. Computational prediction of DTIs can effectively complement experimental wet-lab techniques for the identification of DTIs, which are typically time- and resource-consuming. However, the perf...