Building classifiers using cancer transcriptomes across 33 different cancer-types

Evaluating genome-wide prediction of driver mutations using pan-cancer data

Gene Set Enrichment Class Analysis for heterogeneous RNA sequencing data

The Cancer Genome Atlas datbase | Cancer Classification | Deep Learning | CNN

PANcancer invasiveness analysis using consensus frameworks of RGBM + FGSEA, RGBM + Viper, ARACNE + FGSEA and ARACNE + Viper

In silico methods for predicting miRNA sponge modules

a collection of notebooks containing analysis and visualization methods with publicly available data