Useful links

Questions & Answers

• Bioinformatics StackExchange
• Biostars

Associations, and such

• International Society for Computational Biology
  • Conferences and careers
• bioinformatics.ca (workshops, experts, jobs)
  • job postings are useful for identifying in-demand skill sets

Introductions and tutorials

• No sure what is bioinformatics? Watch this 10 minutes video.

• Nature Web Collection - Computational Biology - NCBI List of Online Bioinformatics Tutorials
• Bioinformatics.org List of Tutorials
• Glittr.org – a curated list of bioinformatics training material

Essential Cellular Biology

• Genetic Education Resources for Teachers by the National Human Genome Research Institute.
• Molecular Biology for Computer Scientists.
  • Chapter 1 from Artificial Intelligence and Molecular Biology. Edited by Lawrence E. Hunter, MIT Press, 1993.
• Biology for computer scientists and AI engineers. GitHub repository by AstraZeneca.

Mathematics

• Essence of linear algebra by 3Blue1Brown

  A series of 15 videos (10 to 15 minutes per video) providing “[a] geometric understanding of matrices, determinants, eigen-stuffs and more.” 6,662,732 views as of September 30, 2019.

• Essence of calculus by 3Blue1Brown

  A series of 12 videos (15 to 20 minutes per video): “The goal here is to make calculus feel like something that you yourself could have discovered.” 2,309,726 views as of September 30, 2019.

• Neural networks by 3Blue1Brown

  8 videos (10 to 27 minutes per video) nearly 50M views as of December 16, 2024.

Machine Learning

• uOttawa Machine Learning - Bilingual (French and English) Machine Learning Tutorial Series published by uOttawa IT Research and Teaching.

Machine Learning in Bioinformatics

• OpenBioML
• Deep Learning for Genetics, Genomics and Metagenomics: Latest developments and New Directions, 5-Day Workshop held at the Banff International Research Station in 2022, 21 hours of videos.
• Machine Learning in Computational Biology. MLCB is an independent conference co-located with NeurIPS. Videos of the past events are available: 2024, 2023, 2019.

Reproducibility for Machine learning in Life Science

• How to avoid machine learning pitfalls: a guide for academic researchers, a paper by Michael A. Lones providing guidelines on common mistakes that occur when using machine learning, and what can be done to avoid them.
• Leakage and the Reproducibility Crisis in ML-based Science, a compilation of evidence that ML-based science faces a reproducibility crisis, with data leakage as a major cause. The authors propose a solution after compiling evidence and conducting investigations.
• Reproducibility standards for machine learning in the life sciences, a 40 minutes video by Michael Hoffman from the University of Toronto.
• 10. Common pitfalls and recommended practices from scikit-learn
• Navigating the pitfalls of applying machine learning in genomics
• Bluesky Social thread discussing the pitfalls of applying machine learning to genomics