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Abstract

Machine learning (ML) approaches are a collection of algorithms that attempt to extract patterns from data and associate such patterns with discrete classes of samples in the data – e.g. given a series of features describing persons, a ML model predicts whether a person is diseased or healthy, or given features of animals, it predicts weather an animal is treated or control, or whether molecules have the potential to interact or not, etc. ML approaches can also find such patterns in an agnostic manner, i.e. without having information about the classes. Respectively, those methods are referred to as supervised and unsupervised ML. A third type of ML is reinforcement learning, which attempts to find a sequence of actions to optimize a utility function, but given its complexity it will not be discussed here. All of these methods are becoming increasingly popular in biomedical research in quite diverse areas including drug design, stratification of patients, medical images analysis, molecular interactions, prediction of therapy outcomes and many more. We describe several ML techniques, pertaining to the first two families mentioned above by illustrating a series of prototypical examples using state-of-the-art computational approaches. We focus on concepts rather than procedures, as our goal is to attract the attention of researchers in biomedicine towards the plethora of powerful ML methods and their potential to leverage basic and applied research programs.