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editorial
. 2019 Apr 16;20:76. doi: 10.1186/s13059-019-1689-0

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