Large-scale machine learning cancer genome analysis for therapeutic target identification
2018
Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
The authors describe and evaluate a combination of transcriptomics and machine-learning approaches to classifying aberrant pathway activity in tumors.This may aid identifying patients who will respond well to a certain anticancer therapy. The algorithm integrates RNA-seq, copy number, and mutations from 33 different cancer types across The Cancer Genome Atlas (TCGA) PanCanAtlas project to predict aberrant molecular states in tumors. Potential biomarkers for choosing cancer treatment are identified.
Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas
Casey S. Greene
Added on: 04-21-2020
[1] https://www.cell.com/cell-reports/fulltext/S2211-1247(18)30389-9?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS2211124718303899%3Fshowall%3Dtrue[2] https://www.technologynetworks.com/informatics/news/machine-learning-helps-point-way-to-hidden-responder-cancer-patients-299537
