Pan-cancer gene set discovery via scRNA-seq for optimal deep learning based downstream tasks.

Abstract excerpt

The application of machine learning to transcriptomics data has led to significant advances in cancer research. However, the high dimensionality and complexity of RNA sequencing (RNA-seq) data pose significant challenges in pan-cancer studies. This study hypothesizes that gene sets derived from single-cell RNA sequencing (scRNA-seq) data will outperform those selected using bulk RNA-seq in pan-cancer downstream...

Topics

• Humans
• Deep Learning
• Neoplasms
• Single-Cell Analysis
• RNA-Seq
• Gene Expression Profiling
• Gene Expression Regulation, Neoplastic