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ComputingLifeSci

A complete computational biology curriculum — 23 courses covering programming, databases, machine learning, genomics and research infrastructure. Written for biologists, not computer scientists.

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Programming for Biology

Python for Biologists
R for Biologists
Perl for Biologists
MATLAB for Biologists
SAS for Biologists
BioJava for Biologists

Data & Databases

SQL for Biological Databases
Bioinformatics Databases
Big Data Mining for Biologists

AI & Machine Learning

Machine Learning for Biologists
Artificial Intelligence for Biologists
MLOps for Biologists
AI Tools for the Bioinformatics Lab

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Machine Learning for Biologists82 chapters

Overview
Chapter 9.1 — Genomics: RNA-Seq, Single-Cell RNA-Seq, GWAS, Variant Classification, and CRISPR
Chapter 9.2 — Disease Prediction, Cancer Prediction, Digital Pathology, Medical Imaging, and Precision Medicine
Chapter 9.3 — Drug Discovery and Protein Structure/Function Prediction
Chapter 9.4 — Metagenomics, Microbiome Analysis, and Microbial Classification

/Machine Learning for Biologists/PART 9 — Machine Learning in Biology

Chapter 42 of 827 min read

Chapter 9.4 — Metagenomics, Microbiome Analysis, and Microbial Classification

1. Learning Objectives Understand how ML is applied to metagenomic and microbiome data Understand ML approaches to microbial taxonomic classification Recognize key challenges specific to microbiome ML (compositionality,…

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PreviousChapter 9.3 — Drug Discovery and Protein Structure/Function Prediction NextOverview