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Highly Accessed Review

Machine learning and genome annotation: a match meant to be?

Kevin Y Yip12345, Chao Cheng67 and Mark Gerstein128*

Author affiliations

1 Program in Computational Biology and Bioinformatics, Yale University, 260/266 Whitney Avenue, New Haven, CT 06520, USA

2 Department of Molecular Biophysics and Biochemistry, Yale University, 260/266 Whitney Avenue, New Haven, CT 06520, USA

3 Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

4 Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

5 CUHK-BGI Innovation Institute of Trans-omics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

6 Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA

7 Institute for Quantitative Biomedical Sciences, Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH 03766, USA

8 Department of Computer Science, Yale University, 51 Prospect Street, New Haven, CT 06511, USA

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Citation and License

Genome Biology 2013, 14:205  doi:10.1186/gb-2013-14-5-205

Published: 29 May 2013

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.