Deep learning in omics: a survey and guideline

• Published in: Briefings in functional genomics Vol. 18; no. 1; pp. 41 - 57
• Main Authors: Zhang, Zhiqiang, Zhao, Yi, Liao, Xiangke, Shi, Wenqiang, Li, Kenli, Zou, Quan, Peng, Shaoliang
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 14.02.2019
• Subjects: Algorithms; Computational Biology - methods; Deep Learning; genomics; Genomics - methods; guidelines; Guidelines as Topic; Humans; proteomics; Proteomics - methods; surveys; Surveys and Questionnaires; Transcriptome; deep learning; omics; gene; neural network; bioinformatics
• DOI: 10.1093/bfgp/ely030
• ISSN: 2041-2649; 2041-2657

Abstract Omics, such as genomics, transcriptome and proteomics, has been affected by the era of big data. A huge amount of high dimensional and complex structured data has made it no longer applicable for conventional machine learning algorithms. Fortunately, deep learning technology can contribute toward resolving these challenges. There is evidence that deep learning can handle omics data well and resolve omics problems. This survey aims to provide an entry-level guideline for researchers, to understand and use deep learning in order to solve omics problems. We first introduce several deep learning models and then discuss several research areas which have combined omics and deep learning in recent years. In addition, we summarize the general steps involved in using deep learning which have not yet been systematically discussed in the existent literature on this topic. Finally, we compare the features and performance of current mainstream open source deep learning frameworks and present the opportunities and challenges involved in deep learning. This survey will be a good starting point and guideline for omics researchers to understand deep learning.