Transcription of A guide to machine learning for biologists
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A guide to machine learning for biologists
REVIEWS. A guide to machine learning for biologists Joe G. Greener 1,2. , Shaun M. Kandathil 1,2. , Lewis Moffat1 and David T. Jones 1 . Abstract | The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning in biology to build informative and predictive models of the underlying biological processes. All machine learning techniques fit models to data; however, the specific methods are quite varied and can at first glance seem bewildering. In this Review, we aim to provide readers with a gentle introduction to a few key machine learning techniques, including the most recently developed and widely used techniques involving deep neural networks.
‘Machine learning’ refers broadly to the process of fit - ting predictive models to data or of identifying informa-tive groupings within data. The field of machine learning essentially attempts to approximate or imitate humans’ ability to recognize patterns, albeit in an objective man-ner, using computation. Machine learning is particularly
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