A comprehensive textbook for bioinformatics perhaps does not exist. It's hard to write one, because the field is moving rapidly, students have varying backgrounds, etc.
Nonetheless, here are some recommendations to learn the foundational concepts in bioinformatics and biology.
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R. Durbin, S. Eddy, A. Krogh, G. MitchisonB (1998). Biological Sequence Analysis. Probabilistic models of proteins and nucleic acids. Cambridge University Press.
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Warren Ewens and Gregory Grant (2001). Statistical Methods in Bioinformatics: An Introduction.
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T.A. Brown. (2023). Genomes 5. CRC Press (or earlier editions)
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Alberts, Heald, Hopkin, Johnson, Morgan, Roberts, Walter (2023). Essential Cell Biology Sixth Edition. W.W. Norton (or earlier editions)
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Christopher Bishop (2006). Pattern Recognition and Machine Learning. https://www.microsoft.com/en-us/research/people/cmbishop/prml-book/
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Tom Mitchell (1997). McGraw Hill. https://www.cs.cmu.edu/~tom/mlbook.html
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Stanford CS229 (Machine Learning) Lecture Notes. https://cs229.stanford.edu/main_notes.pdf
- Machine Learning in Life Sciences. https://github.com/bwgoudey/IntroMLforLifeScienceWorkshopR
Accompanying Colab notebook: https://colab.research.google.com/drive/1k4jeQXKge4ea6EHY5htg8PLo0FcapQdM?usp=sharing
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T.A. Brown (2002). Genomes 2. https://www.ncbi.nlm.nih.gov/books/NBK21128/
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Algorithms in Bioinformatics: A Practical Introduction (2009). CRC Press. https://www.comp.nus.edu.sg/~ksung/algo_in_bioinfo/
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Computer Science Video Lectures. https://github.com/Developer-Y/cs-video-courses?tab=readme-ov-file#computational-biology
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Introduction to Bioinformatics and Computational Biology. https://liulab-dfci.github.io/bioinfo-combio/
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Shervine Amidi. Machine Learning Cheat Sheets. https://stanford.edu/~shervine/teaching/cs-229/
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Bengio, Courville, Bengio (2016). Deep Learning. MIT Press. https://github.com/janishar/mit-deep-learning-book-pdf
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Machine Learning Glossary. https://developers.google.com/machine-learning/glossary