XieResearchGroup

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.

CODE-AE

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

DeepREAL

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

DISAE

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

drug_combine

MolGNN_fewshot

MultiDCP

Physics-aware-Multiplex-GNN

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

PortalLearning

PrePROTAC

PrePROTAC project

XieResearchGroup/Physics-aware-Multiplex-GNN

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

XieResearchGroup/CODE-AE

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

XieResearchGroup/DISAE

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

XieResearchGroup/DeepREAL

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

XieResearchGroup/MolGNN_fewshot

XieResearchGroup/PortalLearning

XieResearchGroup/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.

XieResearchGroup/MultiDCP

XieResearchGroup/PrePROTAC

PrePROTAC project

XieResearchGroup/drug_combine

XieResearchGroup/JDINAC

Joint density-based Differential Interaction Network Analysis and Classification

XieResearchGroup/Lab-wiki-Gitbook

XieResearchGroup/seqCrispr

XieResearchGroup/TransPro

XieResearchGroup/PLANS

Self Training Drug Side Effects Detector

XieResearchGroup/WINTF

weighted, imputed, neighbor-regularized matrix tri-factorization algorithm

XieResearchGroup/AttnToCrispr

XieResearchGroup/CPA

Source code for our paper "Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation" (IJCAI 2020)

XieResearchGroup/Interesting-DL-Materials

List of Interesting Deep Learning Materials

XieResearchGroup/L1000-bayesian

L1000 peak deconvolution based on Bayesian analysis

XieResearchGroup/L1000-repurposing

Drug repurposing with L1000 data

XieResearchGroup/Lab-wiki

Wiki of Xie's Lab

XieResearchGroup/MMAPLE

XieResearchGroup/MXMNet

Source code for our paper "Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures"

XieResearchGroup/pathway_repurposing