Explore a comprehensive 50-minute tutorial on identifying TCGA biomarkers using machine learning techniques. Learn to install necessary packages, import libraries, and utilize TCGA Biolinks for data retrieval. Discover methods for structuring and filtering input data, performing normalization, and conducting PCA analysis. Dive into neural network construction, model training, and saving models as HDF5 files. Extract weights, biases, and genes of interest from the trained model. Conduct gene set enrichment analysis and interpret results. Gain insights into potential limitations of this approach while following along with provided slides and scripts. Perfect for those interested in bioinformatics and machine learning applications in cancer research.
TCGA Biomarkers Identification Using Machine Learning - Complete Walkthrough
LiquidBrain Bioinformatics via YouTube
Overview
Syllabus
Introduction and background
Chapter 1 - Installing packages and importing libraries
Using TCGA Biolinks
Structuring Input data and filtering
PlotMDS from limma and edgeR
Normalization of data
PCA Analysis
Making Train Label and One -hot Encoding
Chapter 2 - Neural network construction
Neural networking Training model fitting
Saving Model as hdf5 files
Extraction weights and bias
Extraction of GOI using weights and bias
Chapter 3 - Gene set enrichment analysis
Results!!!!!!
Some major issues with this approach
Taught by
LiquidBrain Bioinformatics
