Current progress and open challenges for applying deep learning across the biosciences
Abstract
Deep Learning (DL) has recently enabled unprecedented advances in one of the grand challenges in computational biology: the half-century-old problem of protein structure prediction. In this paper we discuss recent advances, limitations, and future perspectives of DL on five broad areas: protein structure prediction, protein function prediction, genome engineering, systems biology and data integration, and phylogenetic inference. We discuss each application area and cover the main bottlenecks of DL approaches, such as training data, problem scope, and the ability to leverage existing DL architectures in new contexts. To conclude, we provide a summary of the subject-specific and general challenges for DL across the biosciences.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Apr 01, 2022
- Source ID
- 10.1038/s41467-022-29268-7
Entities
People
- Advait Balaji
- Amirali Aghazadeh
- Anastasios Kyrillidis
- Bryce Kille
- C. J. Barberan
- Cameron R. Wolfe
- Chen Dun
- Dinler A Antunes
- Luay Nakhleh
- Michael G. Nute
- Mohammadamin Edrisi
- Nicolae Sapoval
- R A Leo Elworth
- Richard G. Baraniuk
- Ruth Dannenfelser
- Todd J Treangen
- Victoria Yao
- Zhi Yan
Organizations
- Intelligence Advanced Research Projects Activity
- National Institute of Allergy and Infectious Diseases
- National Science Foundation
- United States National Library of Medicine