Deep Learning in Biomedical Data Science

Posted: 6 Feb 2019

See all articles by Pierre Baldi

Pierre Baldi

University of California, Irvine - School of Information and Computer Science

Date Written: July 2018

Abstract

Since the 1980s, deep learning and biomedical data have been coevolving and feeding each other. The breadth, complexity, and rapidly expanding size of biomedical data have stimulated the development of novel deep learning methods, and application of these methods to biomedical data have led to scientific discoveries and practical solutions. This overview provides technical and historical pointers to the field, and surveys current applications of deep learning to biomedical data organized around five subareas, roughly of increasing spatial scale: chemoinformatics, proteomics, genomics and transcriptomics, biomedical imaging, and health care. The black box problem of deep learning methods is also briefly discussed.

Suggested Citation

Baldi, Pierre, Deep Learning in Biomedical Data Science (July 2018). Annual Review of Biomedical Data Science, Vol. 1, pp. 181-205, 2018, Available at SSRN: https://ssrn.com/abstract=3329794 or http://dx.doi.org/10.1146/annurev-biodatasci-080917-013343

Pierre Baldi (Contact Author)

University of California, Irvine - School of Information and Computer Science ( email )

P.O. Box 19556
Irvine, CA 62697-3125
United States

Do you have negative results from your research you’d like to share?

Paper statistics

Abstract Views
315
PlumX Metrics