Pinned Repositories
ensemble-learning-GWAS
Application of Two Ensemble Learning Methods to GWAS Data
GeneInteractCRC
LGP
LGP implementation in Python.
LGPWeb
Interactive result visualization website for LGP algorithm
Network-based-Subject-Portrait-Approach
Designed for high-dimensional GWAS dataset, network based subject portrait approach together with the accompanying feature transformation method determines the collective risk impact of multiple genetic interactions for each subject. The resulting transformed features can improve the performance of the predictive analysis in GWAS.
PANDA
prioritizing-genes-GWAS
These java codes have been used to analyze CRC data-set described in the following manuscript: "A Network Approach to Prioritizing Susceptibility Genes for Genome-wide Association Studies"
SMILE
Linear Genetic Programming Implemented in python with sklearn compatible API
vertex-centrality-DILW
thmib's Repositories
thmib/ensemble-learning-GWAS
Application of Two Ensemble Learning Methods to GWAS Data
thmib/Network-based-Subject-Portrait-Approach
Designed for high-dimensional GWAS dataset, network based subject portrait approach together with the accompanying feature transformation method determines the collective risk impact of multiple genetic interactions for each subject. The resulting transformed features can improve the performance of the predictive analysis in GWAS.
thmib/PANDA
thmib/SMILE
Linear Genetic Programming Implemented in python with sklearn compatible API
thmib/GeneInteractCRC
thmib/LGP
LGP implementation in Python.
thmib/LGPWeb
Interactive result visualization website for LGP algorithm
thmib/prioritizing-genes-GWAS
These java codes have been used to analyze CRC data-set described in the following manuscript: "A Network Approach to Prioritizing Susceptibility Genes for Genome-wide Association Studies"
thmib/vertex-centrality-DILW