64 citations · 247 across the 10 of their papers we have counts for
10 papers
Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning
Zhihang Hu, Qinze Yu, Yucheng Guo +5
Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. However, identifying novel drug combinations th…
ProNet DB: A proteome-wise database for protein surface property representations and RNA-binding profiles
Junkang Wei, Jin Xiao, Siyuan Chen +3
The rapid growth in the number of experimental and predicted protein structures and more complicated protein structures challenge users in computational biology for utilizing the s…
Learning to Stop While Learning to Predict
Xinshi Chen, Hanjun Dai, Yu Li +2
There is a recent surge of interest in designing deep architectures based on the update steps in traditional algorithms, or learning neural networks to improve and replace traditio…
RNA Secondary Structure Prediction By Learning Unrolled Algorithms
Xinshi Chen, Yu Li, Ramzan Umarov +2
In this paper, we propose an end-to-end deep learning model, called E2Efold, for RNA secondary structure prediction which can effectively take into account the inherent constraints…
Deep learning in bioinformatics: introduction, application, and perspective in big data era
Yu Li, Chao Huang, Lizhong Ding +3
Deep learning, which is especially formidable in handling big data, has achieved great success in various fields, including bioinformatics. With the advances of the big data era in…
PromID: human promoter prediction by deep learning
Ramzan Umarov, Hiroyuki Kuwahara, Yu Li +2
Computational identification of promoters is notoriously difficult as human genes often have unique promoter sequences that provide regulation of transcription and interaction with…