The DOI matches its Crossref record; 142 of the 146 references it cites were found in a registry or an index.
Learning for Personalized Medicine: A Comprehensive Review From a Deep Learning Perspective
Abstract
With the recent advancements in analyzing high-volume, complex, and unstructured data, modern learning methods are playing an increasingly critical role in the field of personalized medicine. Personalized medicine (i.e., providing tailored medical treatment to individual patients through the identification of common features, including their genetics, inheritance, and lifestyle) has attracted the attention of many researchers in recent years. This paper provides an overview of the research progress in the application of learning methods, with a focus on deep learning in personalized medicine. In particular, three domains of applications are reviewed: drug development, disease characteristic identification, and therapeutic effect prediction. The main objective of this review is to consider the applied methods in detail and to offer insights into their pros and cons. Although having demonstrated advantages in coping with data complexity and nonlinearity and in recognizing features and associating structural data, the studied learning methods are not a panacea to all medical problems. Hence, we discuss the existing research challenges and clarify future study directions.
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“AlphaFold and its underlying deep learning innovations have opened up the next frontiers in protein science,164 including precision medicine.206”
From AlphaFold, Artificial Intelligence (AI), and Allostery (2022) · cites this paper as [206]
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AI in drug discovery and its clinical relevance
Qureshi, R., Irfan, M., Gondal, T. M., Khan, S., Wu, J., Hadi, M. U., Heymach, J., Le, X., Yan, H., & Alam, T. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e17575 · Cited by 319
Déniz-García, A., Fabelo, H. A., Rodríguez-Almeida, A. J., Zamorano, G. B. Z., Castro-Fernández, M., Alberiche-Ruano, M. P., Solvoll, T. G., Granja, C., Schopf, T. R., Callicó, G. M., et al. (2023) · Journal of Medical Internet Research · doi:10.2196/44030 · Cited by 321
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Egger, J., Gsaxner, C., Pepe, A., Pomykala, K. L., Jonske, F., Kurz, M., Li, J., & Kleesiek, J. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106874 · Cited by 289
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Multi-Disease Prediction Based on Deep Learning: A Survey
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Metadata
- Authors
- Zhang, S., Bamakan, S. M. H., Qu, Q., & Li, S.
- Affiliations
- Chinese Academy of Sciences (CN)
- Venue
- IEEE Reviews in Biomedical Engineering
- Published
- 2019
- Volume
- 12
- Pages
- 194-208
- ISSN
- 1937-3333 (ISSN-L)
- 1941-1189
- OpenAlex
- W2887975519
- Cited by
- 125
- References
- 146
- Citations by year
- 2026: 7 · 2025: 21 · 2024: 30 · 2023: 20 · 2022: 19 · 2021: 17 · 2020: 9 · 2019: 2
- FWCI
- 0.81
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Reviews in Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- Funders
- CAS Pioneer Hundred Talents Program, China (Y84402, 2017)
- China Postdoctoral Science Foundation (2018M633186, 2018)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Keywords
- personalized medicine, deep learning, drug development, genetic data, machine learning in medicine
- MeSH terms
- Drug Development, Deep Learning, Humans, Research, Precision Medicine
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