Applications of Deep Learning in Biomedicine
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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Machine learning techniques utilizing Deep Neural Networks (DNNs) are able to capture high-level dependencies in healthcare data.
“the machine learning techniques utilizing the Deep Neural Networks (DNNs) are able to capture high-level dependencies in the healthcare data [32].”
From Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare (2018) · cites this paper as [32]
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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
- Publisher
- American Chemical Society (ACS)
- 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
- Licence
- 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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