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ML for Biology

Apply machine learning algorithms to biological problems. From protein classification to drug response prediction, learn how AI is revolutionizing computational biology research.

ML for Biology

Syllabus

Foundations2 lessons
  1. What ML Is (and Isn't) in Biology10 min
  2. Framing a Biological Problem as ML11 min
Features & Splits3 lessons
  1. Encoding Sequences: k-mers and One-Hot11 min
  2. Structural, Physicochemical, and Expression Features12 min
  3. Train/Val/Test Splits and Sequence-Similarity Leakage11 min
Modeling & Evaluation5 lessons
  1. The scikit-learn Workflow: Classification and Regression11 min
  2. Logistic Regression and SVMs11 min
  3. Random Forests and Gradient Boosting12 min
  4. Evaluation Metrics for Imbalanced Biology11 min
  5. Overfitting, Regularization, and Cross-Validation12 min
Deep Learning & Applications3 lessons
  1. Deep Learning Teaser: CNNs on Sequence12 min
  2. Applications: Variant Effect, Protein Function, Expression12 min
  3. Pitfalls, Reproducibility, and Where to Next11 min