Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Editorial
. 2019 Apr 16;20(1):76.
doi: 10.1186/s13059-019-1689-0.

Machine learning and complex biological data

Chunming Xu  1   2 , Scott A Jackson  3
Affiliations
Editorial

Machine learning and complex biological data

Chunming Xu et al. Genome Biol. .

Abstract

Machine learning has demonstrated potential in analyzing large, complex biological data. In practice, however, biological information is required in addition to machine learning for successful application.

PubMed Disclaimer

Conflict of interest statement

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Machine learning using complex biological data. High-throughput data generation techniques for different biological aspects are shown (left). ATAC-seq assay for transposase-accessible chromatin using sequencing, ChIP-seq chromatin immunoprecipitation sequencing, DNase-seq DNase I hypersensitive sites sequencing, GC-MS gas chromatography-mass spectrometry, LC-MS liquid chromatography–mass spectrometry, lncRNA-seq long non-coding RNA sequencing, NMR nuclear magnetic resonance, RNA-seq RNA sequencing, smRNA-seq small RNA sequencing, WES whole exome sequencing, WGBS whole-genome bisulfite sequencing, WGS whole genome sequencing, Hi-C chromatin conformation capture combined with deep sequencing, iTRAQ isobaric tags for relative and absolute quantification
Fig. 2
Fig. 2
Interpretation of machine learning model. Model information may be interpreted directly or be further processed for better understanding
See this image and copyright information in PMC

References

    1. Zitnik M, Nguyen F, Wang B, Leskovec J, Goldenberg A, Hoffman MM. Machine learning for integrating data in biology and medicine: principles, practice, and opportunities. Inf Fusion. 2019;50:71–91. doi: 10.1016/j.inffus.2018.09.012. - DOI - PMC - PubMed
    1. Altman N, Krzywinski M. The curse(s) of dimensionality. Nat Methods. 2018;15:399–400. doi: 10.1038/s41592-018-0019-x. - DOI - PubMed
    1. Bzdok D, Altman N, Krzywinski M. Statistics versus machine learning. Nat Methods. 2018;15:233–234. doi: 10.1038/nmeth.4642. - DOI - PMC - PubMed
    1. Libbrecht MW, Noble WS. Machine learning applications in genetics and genomics. Nat Rev Genet. 2015;16:321–332. doi: 10.1038/nrg3920. - DOI - PMC - PubMed
    1. Zou J, Huss M, Abid A, Mohammadi P, Torkamani A, Telenti A. A primer on deep learning in genomics. Nat Genet. 2019;51:12–18. doi: 10.1038/s41588-018-0295-5. - DOI - PMC - PubMed

Publication types

LinkOut - more resources