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https://github.com/AstraZeneca/biology-for-ai
learning biology syllabus, geared for machine learning folks
https://github.com/AstraZeneca/biology-for-ai
Last synced: 1 day ago
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learning biology syllabus, geared for machine learning folks
- Host: GitHub
- URL: https://github.com/AstraZeneca/biology-for-ai
- Owner: AstraZeneca
- Created: 2021-02-01T10:18:56.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-12-05T16:29:03.000Z (almost 2 years ago)
- Last Synced: 2024-06-14T00:20:53.743Z (4 months ago)
- Size: 27.3 KB
- Stars: 79
- Watchers: 4
- Forks: 10
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Biology for computer scientists and AI engineers
a syllabus and some resources for learning biology, geared for machine learning folks
## Find some motivation
Biology is fractal in its complexity and superbly interesting.
As a computer scientist or machine learning scientist many of the concepts you encounter will resonate with you.
The application of computing and engineering approaches promises to transform and advance our understanding of biology, an argument elegantly made by Yuri Lazbenik in his [Can a biologist fix a radio? Or, what I learned while studying apoptosis](https://www.cell.com/cancer-cell/fulltext/S1535-6108(02)00133-2)
If you needed some more motivation (and guidance on how) to learn biology, James Somers has a magnificent essay:
If you need hope here is the view of Uri Alon, one of the founder of the discipline of "systems biology":
## Courses
The best introductory course in biology is arguably Eric Lander's (&co) MIT 7.00x: which can be audited for free; the material is available also [on OCW](https://ocw.mit.edu/courses/biology/7-01sc-fundamentals-of-biology-fall-2011/)
Uri Alon also has released a great **system biology** course available online:
Another course on **systems medicine** is available from:
This is a **systems biology** course by Bernhard Palsson:
## Books
[Cell biology by the numbers](http://book.bionumbers.org/) is an excellent entry point to explore some of the key numbers of cell biology.
The [eight day of creation](https://www.amazon.com/The-Eighth-Day-Creation-Commemorative/dp/0879694785) is "the best history of recent biological science yet published"
[A Very Short Introduction to Molecular Biology](https://www.veryshortintroductions.com/view/10.1093/actrade/9780198723882.001.0001/actrade-9780198723882) a pocket guide that introduces you to the core concepts and terminology.
[Bioinformatics Algorithms Textbook](https://www.bioinformaticsalgorithms.org/) - this book could also be considered a self-contained course. Learning about biology can be easier for some CS people when explained through algorithms, and this book is really good at merging biology and code into coherent concepts 🙂
## Smaller "primers"
- [Molecular biology for computer scientists](https://tandy.cs.illinois.edu/Hunter_MolecularBiology.pdf) a 45 pages primer that covers "enough background for a computer scientists to understand[..] the daunting intricacies of existing biological knowledge and its extensive technical vocabulary"
- [A Biology Primer for Computer Scientists](http://web.stanford.edu/class/cs173/papers/bioprimer.pdf) 18 pages of notes which "outline a minimal background for computer scientists wishing to deal with computer algorithms relevant to problems in molecular biology"
- [A Computer Scientist’s Guide to Cell Biology](https://wwcohen.github.io/GuideToBiology-sampleChapter-release1.4.pdf) 40 pages of notes from a computer scientists who has been learning the basics of cell biology