I have recently engaged with a collaboration, which requires me to construct, then train an unsupervised artificial neural network (ANN).

However, I have only a very coarse understanding of what machine-learning classifiers try to do, and how they work. For example, Chen et al.'s random forest (RF) classifier (ensemble of decision trees) for protein-protein interaction prediction. Additionally, I have very little idea about which types of machine-learning are useful to different classes of Biological problems.

So -if they exist- I would very much appreciate information on books that discuss applications of machine-learning classifiers in Biology/Bioinformatics. Preferable criteria are:

  • Intuitively introduces key concepts.

  • Illustrates a variety of Biological/Bioinformatics problems and their solutions by appropriate types of machine-learning (e.g. when is it appropriate to use ANNs versus RF classifiers?)

  • Relatively short (I'm planning to read from cover to cover).



At NIPS 2016 most ANN implementations I saw were related to biology were trained on imaging data, thus the kind of comprehensive book you are looking for probably doesn't exist yet.

However, if you utilize the search feature on Amazon.com you'll find:

Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition

Artificial Neural Networks (Methods in Molecular Biology)

and this, which seems to best meet your need:

Neural Networks and Genome Informatics, Volume 1 (Methods in Computational Biology and Biochemistry)


I own a book entitled Bioinformatics, The Machine Learning Approach, by Baldi and Brunak. I haven’t done more than glance at it, but it might be of interest. It’s more geared to bioinformatics than general computational biology.


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