Hunter College, The City University of New York

Hunter College, The City University of New York's repositories

Physics-aware-Multiplex-GNN

Code for our Nature Scientific Reports paper "A universal framework for accurate and efficient geometric deep learning of molecular systems"

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Coherent Deconfounding Autoencoder (CODE-AE) can extract both common biological signals shared by incoherent samples and private representations unique to each data set, transfer knowledge learned from cell line data to tissue data, and separate confounding factors from them

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DISAE

MSA-Regularized Protein Sequence Transformer toward Predicting Genome-Wide Chemical-Protein Interactions: Application to GPCRome Deorphanization

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DeepREAL

DeepREAL: A Deep Learning Powered Multi-scale Modeling Framework Towards Predicting Out-of-distribution Receptor Activity of Ligand Binding

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MolGNN_fewshot

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PortalLearning

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CLEIT

Cross-LEvel Information Transmission network (CLEIT) aims to represent the asymmetrical multi-level organization of the biological system by integrating multiple incoherent omics data. It first learns the latent representation of the high-level domain then uses it as ground-truth embedding to improve the representation learning of the low-level domain in the form of contrastive loss.

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