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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.

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Conflict of interest statement

Competing interests

The authors declare that they have no competing interests.

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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

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