2 projects
imap
The integration of single-cell RNA-sequencing datasets from multiple sources is critical for deciphering cell-cell heterogeneities and interactions in complex biological systems. We present a novel unsupervised batch removal framework, called iMAP, based on two state-of-art deep generative models – autoencoders and generative adversarial networks.
visar
This project aims to train neural networks by compound-protein interactions and provides interpretation of the learned model by interactively showing transformed chemical landscape and visualized SAR for chemicals of interest.
