JieZheng-ShanghaiTech
Jie is a tenured Associate Professor at ShanghaiTech, working on Bioinformatics, Data science, and AI for science. He worked at NTU, Singapore and NIH, USA.
School of Information Science and Technology, ShanghaiTech UniversityPudong District, Shanghai, China
Pinned Repositories
HiCoEx
A supervised learning model based on Graph Neural Network to predict gene co-expression from chromatin contacts
KG4SL
Synthetic lethality (SL) is a promising gold mine for the discovery of anti-cancer drug targets. KG4SL is the first graph neural network (GNN)-based model that uses knowledge graph for SL prediction.
KR4SL
LukePi
MiT4SL
MiT4SL is the first machine learning model for cross cell line prediction of synthetic lethal (SL) gene pairs. It uses a novel method of triplet representation learning to encode cell line information by integrating multi-omics data of gene expression, PPI network and protein sequences, etc.
NSF4SL
NSF4SL is a negative-sample-free model for prediction of synthetic lethality (SL) based on a self-supervised contrastive learning framework.
PiLSL
PiLSL is a pairwise interaction learning-based graph neural network (GNN) model for prediction of synthetic lethality (SL) as anti-cancer drug targets. It learns the representation of pairwise interaction between two genes from a knowledge graph (KG).
SL_benchmark
Benchmarking study of machine learning methods for prediction of synthetic lethality
SynLethDB
SynLethDB is a comprehensive database (and knowledgebase) for synthetic lethality, a promising strategy of cancer therapeutics and drug discovery
TMELand
A software tool for modeling and visualization of Waddington's epigenetic landscape based on dynamical models of gene regulatory network (GRN).
JieZheng-ShanghaiTech's Repositories
JieZheng-ShanghaiTech/KG4SL
Synthetic lethality (SL) is a promising gold mine for the discovery of anti-cancer drug targets. KG4SL is the first graph neural network (GNN)-based model that uses knowledge graph for SL prediction.
JieZheng-ShanghaiTech/SL_benchmark
Benchmarking study of machine learning methods for prediction of synthetic lethality
JieZheng-ShanghaiTech/HiCoEx
A supervised learning model based on Graph Neural Network to predict gene co-expression from chromatin contacts
JieZheng-ShanghaiTech/PiLSL
PiLSL is a pairwise interaction learning-based graph neural network (GNN) model for prediction of synthetic lethality (SL) as anti-cancer drug targets. It learns the representation of pairwise interaction between two genes from a knowledge graph (KG).
JieZheng-ShanghaiTech/TMELand
A software tool for modeling and visualization of Waddington's epigenetic landscape based on dynamical models of gene regulatory network (GRN).
JieZheng-ShanghaiTech/KR4SL
JieZheng-ShanghaiTech/MiT4SL
MiT4SL is the first machine learning model for cross cell line prediction of synthetic lethal (SL) gene pairs. It uses a novel method of triplet representation learning to encode cell line information by integrating multi-omics data of gene expression, PPI network and protein sequences, etc.
JieZheng-ShanghaiTech/NSF4SL
NSF4SL is a negative-sample-free model for prediction of synthetic lethality (SL) based on a self-supervised contrastive learning framework.
JieZheng-ShanghaiTech/SynLethDB
SynLethDB is a comprehensive database (and knowledgebase) for synthetic lethality, a promising strategy of cancer therapeutics and drug discovery
JieZheng-ShanghaiTech/LukePi
JieZheng-ShanghaiTech/PIKE-R2P
JieZheng-ShanghaiTech/MGE4SL
In this project, we developed a Multi-Graph Ensemble (MGE) framework combining graph neural network and existing knowledge about genes to predict synthetic lethal (SL) gene pairs.
JieZheng-ShanghaiTech/GENNDTI
JieZheng-ShanghaiTech/NexLeth
JieZheng-ShanghaiTech/boolean-t2dm
In this project, we constructed a Boolean network model for the human pancreatic beta-cell, for study of Type 2 Diabetes (T2D).
JieZheng-ShanghaiTech/Meta-CapSL
Meta-CapSL is a meta-learning model for predicting cancer-specific synthetic lethality (SL) as drug targets under low-data scenarios.