1 citations · 2 across the 6 of their papers we have counts for
6 papers
Phenotypic Profile-Informed Generation of Drug-Like Molecules via Dual-Channel Variational Autoencoders
Hui Liu, Shiye Tian, Xuejun Liu
The de novo generation of drug-like molecules capable of inducing desirable phenotypic changes is receiving increasing attention. However, previous methods predominantly rely on ex…
Attention-aware contrastive learning for predicting T cell receptor-antigen binding specificity
Yiming Fang, Xuejun Liu, Hui Liu
It has been verified that only a small fraction of the neoantigens presented by MHC class I molecules on the cell surface can elicit T cells. The limitation can be attributed to th…
Attention-wise masked graph contrastive learning for predicting molecular property
Hui Liu, Yibiao Huang, Xuejun Liu +1
Accurate and efficient prediction of the molecular properties of drugs is one of the fundamental problems in drug research and development. Recent advancements in representation le…
Contrastive learning-based computational histopathology predict differential expression of cancer driver genes
Haojie Huang, Gongming Zhou, Xuejun Liu +4
Digital pathological analysis is run as the main examination used for cancer diagnosis. Recently, deep learning-driven feature extraction from pathology images is able to detect ge…
Graph2MDA: a multi-modal variational graph embedding model for predicting microbe-drug associations
Lei Deng, Yibiao Huang, Xuejun Liu +1
Accumulated clinical studies show that microbes living in humans interact closely with human hosts, and get involved in modulating drug efficacy and drug toxicity. Microbes have be…
DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations
J. Wang, X. Liu, S. Shen +2
Drug combination therapy has become a increasingly promising method in the treatment of cancer. However, the number of possible drug combinations is so huge that it is hard to scre…