Cited by 701Computational Drug Discovery MethodsMachine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Applications of Deep Learning in Biomedicine

Mamoshina, Polina, Vieira, Armando, Lane, Eugene, Zhavoronkov, Alex·2016·Molecular Pharmaceutics

Abstract

Increases in throughput and installed base of biomedical research equipment led to a massive accumulation of -omics data known to be highly variable, high-dimensional, and sourced from multiple often incompatible data platforms. While this data may be useful for biomarker identification and drug discovery, the bulk of it remains underutilized. Deep neural networks (DNNs) are efficient algorithms based on the use of compositional layers of neurons, with advantages well matched to the challenges -omics data presents. While achieving state-of-the-art results and even surpassing human accuracy in many challenging tasks, the adoption of deep learning in biomedicine has been comparatively slow. Here, we discuss key features of deep learning that may give this approach an edge over other machine learning methods. We then consider limitations and review a number of applications of deep learning in biomedical studies demonstrating proof of concept and practical utility.

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Crossref · 10.1021/acs.molpharmaceut.5b00982

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48 of 48 references verified0 citation errors · 0 not checkable

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Authors
Mamoshina, P., Vieira, A., Lane, E., & Zhavoronkov, A.
Affiliations
  • Johns Hopkins University (US)
  • RedZebra Analytics, 1 Quality Court, London, WC2A 1HR, U.K.
Corresponding author
Zhavoronkov, Alex
Venue
Molecular Pharmaceutics
Published
2016-03-23
Volume
13
Issue
5
Pages
1445-1454
ISSN
  • 1543-8384 (ISSN-L)
  • 1543-8392
OpenAlex
W2306570595
Cited by
701
References
48
Citations by year
2026: 23 · 2025: 46 · 2024: 68 · 2023: 84 · 2022: 83 · 2021: 84 · 2020: 90 · 2019: 89 · 2018: 76 · 2017: 39 · 2016: 19
FWCI
5.74
Type
journal article
Language
en
Open access
closed
Locations
  • Molecular Pharmaceutics (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
deep learning, deep neural network, biomedical data integration, biomarker discovery, drug discovery, high-dimensional data
MeSH terms
Machine Learning, Animals, Humans, Nerve Net, Biomarkers, Biomedical Research, Drug Discovery

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