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).
- Stack Biology: Machine-learning for light microscopy problems to solve (I would consider this question a sub-class of the present question; personally, I am more interested in the sequence analysis / genetics domain of Bioinformatics problems)
- Stack Biology: Historical connection between neuroscience and machine intelligence
- Blogs about deep learning implementations in Biology
- GitXiv: Collaborative on-going (and also published) computer science projects pertaining to machine-learning (search using relevant Biology-related queries to filter)