Table 2.

Categories of tasks being solved with omics data and deep learning.

	Description	Examples of Tasks
Biological	Modeling for biological insight or for prediction of biological attributes	Identify functional units in DNA sequences Elucidate interactions among genes or proteins Predict attributes of proteins or DNA sequences
Biomedical	Modeling related to understanding disease, diagnostics, and therapeutics	Cluster biological samples into cancer subtypes Predict whether patient is low- or high-risk Identify biomarkers for early-stage disease
Drug Research	Modeling directly assisting the discovery and development of pharmaceutics	Predict the effect a chemical agent will have on cells, gene expression, or proteins From gene expression profiles, predict which chemical agent was applied on tissue
Bioinformatics	Modeling to assist or automate technical bioinformatics tasks	From a stack of contigs generate consensus sequences From a batch of mass spectra generate a single integrated spectrum Separate peaks from noise in spectra data