e-learning
PAPAA PI3K_OG: PanCancer Aberrant Pathway Activity Analysis
Abstract
Signaling pathways are among the most commonly altered across different tumor types. Many tumors possess at least one driver alteration and nearly half of such alterations are potentially targeted by currently available drugs. A recent study in TCGA tumors has identified patterns of somatic variations and mechanisms in 10 canonical pathways
About This Material
This is a Hands-on Tutorial from the GTN which is usable either for individual self-study, or as a teaching material in a classroom.
Questions this will address
- How to predict aberrant pathway activity in The Cancer Genome Atlas (TCGA) using Machine learning approaches?
Learning Objectives
- Learn to predict aberrant pathway activity using RNA-Seq data, mutational status and copy number variation data from TCGA.
- Apply logistic regression based machine learning algorithms on TCGA data.
Licence: Creative Commons Attribution 4.0 International
Keywords: Machine learning, Pan-cancer, Statistics and machine learning, cancer biomarkers, oncogenes and tumor suppressor genes
Target audience: Students
Resource type: e-learning
Version: 7
Status: Active
Prerequisites:
Introduction to Galaxy Analyses
Learning objectives:
- Learn to predict aberrant pathway activity using RNA-Seq data, mutational status and copy number variation data from TCGA.
- Apply logistic regression based machine learning algorithms on TCGA data.
Date modified: 2023-11-09
Date published: 2021-05-06
Scientific topics: Statistics and probability
Activity log
