50 citations · 76 across the 3 of their papers we have counts for
6 papers
Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery
Zhengyang Wang, Meng Liu, Youzhi Luo +8
Properties of molecules are indicative of their functions and thus are useful in many applications. With the advances of deep learning methods, computational approaches for predict…
Deep Low-Shot Learning for Biological Image Classification and Visualization from Limited Training Samples
Lei Cai, Zhengyang Wang, Rob Kulathinal +2
Predictive modeling is useful but very challenging in biological image analysis due to the high cost of obtaining and labeling training data. For example, in the study of gene inte…
Line Graph Neural Networks for Link Prediction
Lei Cai, Jundong Li, Jie Wang +1
We consider the graph link prediction task, which is a classic graph analytical problem with many real-world applications. With the advances of deep learning, current link predicti…
Deep Learning of High-Order Interactions for Protein Interface Prediction
Yi Liu, Hao Yuan, Lei Cai +1
Protein interactions are important in a broad range of biological processes. Traditionally, computational methods have been developed to automatically predict protein interface fro…
Dense Transformer Networks
Jun Li, Yongjun Chen, Lei Cai +2
The key idea of current deep learning methods for dense prediction is to apply a model on a regular patch centered on each pixel to make pixel-wise predictions. These methods are l…
Multi-Stage Variational Auto-Encoders for Coarse-to-Fine Image Generation
Lei Cai, Hongyang Gao, Shuiwang Ji
Variational auto-encoder (VAE) is a powerful unsupervised learning framework for image generation. One drawback of VAE is that it generates blurry images due to its Gaussianity ass…