YCSGP
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MuSe-GNN
MuSe-GNN PublicForked from HelloWorldLTY/MuSe-GNN
[NeurIPS 2023] Official codes of "MuSe-GNN: Learning Unified Gene Representation From Multimodal Biological Graph Data"
Python
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Geneverse
Geneverse PublicForked from HelloWorldLTY/Geneverse
[EMNLP 2024] This is the official code repo for the paper: Geneverse: A collection of Open-source Multimodal Large Language Models for Genomic and Proteomic Research.
Python
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SDPRX
SDPRX PublicForked from eldronzhou/SDPRX
A statistical method for cross-population prediction of complex traits
Python
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SDPR
SDPR PublicForked from eldronzhou/SDPR
A fast and robust Bayesian nonparametric method for prediction of complex traits using GWAS summary statistics
C
Repositories
- YCSGP.github.io Public
YCSGP/YCSGP.github.io’s past year of commit activity - PERADIGM Public Forked from JJJJJasonZheng/PERADIGM
PERADIGM: Phenotype Embedding Similarity-based Rare Disease Gene Mapping
YCSGP/PERADIGM’s past year of commit activity - Pathway-Analysis-using-Random-Forest Public
An R code for pathway-based classification and regression using Random Forests.
YCSGP/Pathway-Analysis-using-Random-Forest’s past year of commit activity - LORS Public
A Low-Rank representation and Sparse regression for eQTL mapping. This algorithm accounts for confounding factors such as unobserved covariates, experimental artifacts, and unknown environmental perturbations.
YCSGP/LORS’s past year of commit activity - GWAS-with-MRF-pathway Public
This is a program to implement the Markov Random Field (MRF) method to incorporate pathway topology for genome wide association studies.
YCSGP/GWAS-with-MRF-pathway’s past year of commit activity - viRandomForests Public
Variable importance-weighted Random Forests (viRandomForests) is an R package, which samples features according to their variable importance scores, and then selects the best split from the randomly selected features, to improved prediction accuracy in the presence of weak signals and large noises.
YCSGP/viRandomForests’s past year of commit activity - GWAS.PC Public
GWAS.PC (GWAS Power Calculation) is an R package that does power analysis in genome wide association studies. In particular, genotyping error is considered in power calculation.
YCSGP/GWAS.PC’s past year of commit activity - gbr Public
This is an R package implementing a post-GWAS prioritization algorithm, which incorporates the rewiring information of co-expression network to prioritize GWAS signals.
YCSGP/gbr’s past year of commit activity
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