JieZheng (@JieZheng-ShanghaiTech)
  • Stars
    star
    68
  • Global Rank 273,336 (Top 10 %)
  • Followers 23
  • Following 1
  • Registered over 5 years ago
  • Most used languages
    Python
    72.7 %
  • Location 🇨🇳 China
  • Country Total Rank 34,079
  • Country Ranking
    Python
    6,645

Top repositories

1

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.
Python
30
star
2

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).
Python
9
star
3

HiCoEx

A supervised learning model based on Graph Neural Network to predict gene co-expression from chromatin contacts
Jupyter Notebook
6
star
4

SL_benchmark

Benchmarking study of machine learning methods for prediction of synthetic lethality
Jupyter Notebook
5
star
5

TMELand

A software tool for modeling and visualization of Waddington's epigenetic landscape based on dynamical models of gene regulatory network (GRN).
Python
4
star
6

KR4SL

Python
3
star
7

SynLethDB

SynLethDB is a comprehensive database (and knowledgebase) for synthetic lethality, a promising strategy of cancer therapeutics and drug discovery
3
star
8

NSF4SL

NSF4SL is a negative-sample-free model for prediction of synthetic lethality (SL) based on a self-supervised contrastive learning framework.
Python
3
star
9

PIKE-R2P

Jupyter Notebook
2
star
10

LukePi

Python
2
star
11

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.
Python
1
star
12

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.
Python
1
star