31 skills found · Page 1 of 2
KarchinLab / 2020plusClassifies genes as an oncogene, tumor suppressor gene, or as a non-driver gene by using Random Forests
hanasusak / CDrivercDriver R package for finding candidate driver genes in cancers
gevaertlab / AMARETTORegulatory network inference and driver gene evaluation using integrative multi-omics analysis and penalized regression
egeulgen / DriveRPrioritizing Cancer Driver Genes Using Genomics Data
WGLab / IcagesiCAGES (integrated CAncer GEnome Score) is an effective tool for prioritizing cancer driver genes for a patient
zjupgx / ModigMODIG: Integrating Multi-Omics and Multi-Dimensional Gene Network for Cancer Driver Gene Identification based on Graph Attention Network Model
danro9685 / ASCETICASCETIC (Agony-baSed Cancer EvoluTion InferenCe) is a novel framework for the inference of a set of statistically significant temporal patterns involving alternations in driver genes from cancer genomics data. Manuscript published at: https://www.nature.com/articles/s41467-023-41670-3
NWPU-903PR / PNCThe PNC package is to identify personalized driver genes of an individual patient by using network control principle .
Shamir-Lab / PRODIGYPersonalized prioritization of driver genes in cancer
reimandlab / ActiveDriverWGSRActiveDriverWGSR is an R package for discovery of cancer driver genes and non-coding elements in whole genome sequencing data
andreamrau / EDGE In TCGASource code to reproduce results from "Exploring Drivers of Gene Expression in The Cancer Genome Atlas" by Rau et al. (2017)
weiba / MNGCLMulti-graph contrastive learning for cancer driver gene identification
weiba / MRNGCNIntegrating multiple networks to identify cancer driver genes based on heterogeneous graph convolution with self-attention mechanism
MartinFXP / DawnRankdriver gene identification algorithm DawnRank 2014
KarchinLab / Probabilistic2020Simulates somatic mutations, and calls statistically significant oncogenes and tumor suppressor genes based on a randomization-based test
montilab / IEDGEintegrative analysis of (epi-)DNA and gene expression data for the identification of candidate cancer drivers
hoarjour / HistCodeContrastive learning-based computational histopathology predict differential expression of cancer driver genes
huynguyen250896 / DrGADrGA is a novel R package that has been developed based on the idea of our recent driver gene analysis scheme. It wholly automates the analysis process and attached improvements to maximize user experience with the highest convenience. In particular, it facilitates users with limited IT backgrounds and rapidly creates consistent and reproducible results. We describe the usage of the DrGA on driver genes of human breast cancer using a multi-omics dataset. Besides, we also provide users with another potential application of DrGA on analyzing genomic biomarkers of a complex disease from other species.
sigven / GeneOncoXHuman gene annotations for the oncology domain
bio-ontology-research-group / PredCANOntology-based prediction of cancer driver genes
