Predicting drug−disease associations via sigmoid kernel-based convolutional neural networks

• Published in: Journal of translational medicine Vol. 17; no. 1; pp. 382 - 11
• Main Authors: Jiang, Han-Jing, You, Zhu-Hong, Huang, Yu-An
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 20.11.2019; BioMed Central; BMC
• Subjects: Analysis; Artificial neural networks; Asthma; Bioinformatics; Cable television broadcasting industry; Case studies; Computational biology; Computer applications; Convolutional Neural Networks; Data mining; Datasets; Deep learning; Disease; Drug development; Drugs; Gene expression; Medical research; Methods; Natural language processing; Neural networks; Performance evaluation; Prediction models; Random forest; Recommender systems; Semantics; Sigmoid kernel; United States; Random forest; Sigmoid kernel; Convolutional Neural Networks

In the process of drug development, computational drug repositioning is effective and resource-saving with regards to its important functions on identifying new drug-disease associations. Recent years have witnessed a great progression in the field of data mining with the advent of deep learning. An...