title: Machine learning applications in genetics and genomics.
abstract: The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets. Here, we provide an overview of machine learning applications for the analysis of genome sequencing data sets, including the annotation of sequence elements and epigenetic, proteomic or metabolomic data. We present considerations and recurrent challenges in the application of supervised, semi-supervised and unsupervised machine learning methods, as well as of generative and discriminative modelling approaches. We provide general guidelines to assist in the selection of these machine learning methods and their practical application for the analysis of genetic and genomic data sets.
authors.affiliation: Department of Computer Science and Engineering, University of Washington, 185 Stevens Way, Seattle, Washington 98195-2350, USA.
authors.forename: Maxwell W
authors.initials: MW
authors.lastname: Libbrecht
authors.affiliation: 1] Department of Computer Science and Engineering, University of Washington, 185 Stevens Way, Seattle, Washington 98195-2350, USA. [2] Department of Genome Sciences, University of Washington, 3720 15th Ave NE Seattle, Washington 98195-5065, USA.
authors.forename: William Stafford
authors.initials: WS
authors.lastname: Noble
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D001185:Artificial Intelligence; D001483:Base Sequence; D003198:Computer Simulation; D016002:Discriminant Analysis; D005826:Genetics, Medical; D023281:Genomics; D006801:Humans; D008957:Models, Genetic; D058977:Molecular Sequence Annotation