title: Machine learning and genome annotation: a match meant to be?
abstract: By its very nature, genomics produces large, high-dimensional datasets that are well suited to analysis by machine learning approaches. Here, we explain some key aspects of machine learning that make it useful for genome annotation, with illustrative examples from ENCODE.
authors.forename: Kevin Y
authors.initials: KY
authors.lastname: Yip
authors.forename: Chao
authors.initials: C
authors.lastname: Cheng
authors.forename: Mark
authors.initials: M
authors.lastname: Gerstein
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D015894:Genome, Human; D023281:Genomics / Q000379:methods*; D006801:Humans; D058977:Molecular Sequence Annotation / Q000379:methods*; D008969:Molecular Sequence Data