Machine Learning for Biology
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Machine Learning for Biology

We’ve essentially found a way to make sand think. It’s miraculous.
Demis Hassabis Nobel Laureate · Co-Founder and Chair, Google DeepMind · Chief Scientist, Alphabet · Founder and CEO, Isomorphic Labs“A Framework for Frontier AI and the Dawning of a New Age”, 14 July 2026

Qasim Hussain

Beta edition. This book will be revised, corrected and extended continuously for some time yet. Suggestions for its improvement are most welcome, through an issue on its repository or a message on LinkedIn.

Contents

  1. Preface
  2. Part I · Learning from labels
  3. 1Predicting antibiotic resistance from a genomeLogistic regression
  4. 2What does a cell show to the immune system?Decision tree
  5. 3Is a gene a fact or a call?Random forest
  6. 4What happens to the patients who are still alive?Gradient boosting
  7. 5Gut bacteria and cardiovascular riskLogistic regression (support vector machine planned)
  8. 6Neoantigen immunogenicity for cancer vaccine designNeural network
  9. Part II · Learning without labels
  10. 7Immune cell subtype discovery from single-cell RNA-seqk-means
  11. Epilogue