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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 38 of 821 min read

Overview

(Applying everything learned so far to real biological application domains)

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PreviousChapter 8.3 — ROC Curves, AUC, and Model Calibration NextChapter 9.1 — Genomics: RNA-Seq, Single-Cell RNA-Seq, GWAS, Variant Classification, and CRISPR