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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”
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Stojadinovic, A., Bilchik, A. J., Smith, D. D., Eberhardt, J. S., Ward, E. B., Nissan, A., Johnson, E. K., Protić, M., Peoples, G. E., Avital, I., et al. (2012) · Annals of Surgical Oncology · doi:10.1245/s10434-012-2555-4 · Cited by 61
Exploiting Convolutional Neural Network for Risk Prediction with Medical Feature EmbeddingPreprint
Che, Z., Cheng, Y., Sun, Z., & Liu, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.07474 · Cited by 60
Jang, I. S., Neto, E. C., Guinney, J., Friend, S. H., & Margolin, A. A. (2013) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789814583220_0007 · Cited by 57
Integrative Gene Network Construction to Analyze Cancer Recurrence Using Semi-Supervised Learning
Park, C., Ahn, J., Kim, H., & Park, S. (2014) · PLoS ONE · doi:10.1371/journal.pone.0086309 · Cited by 55
Medical Data Mining on the Internet: Research on a Cancer Information System
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SMC: A Practical Schema for Privacy-Preserved Data Sharing over Distributed Data Streams
Liu, S., Qu, Q., Chen, L., & Ni, L. M.-S. (2015) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2015.2498156 · Cited by 40
Self-citationEfficient Online Summarization of Large-Scale Dynamic Networks
Qu, Q., Liu, S., Zhu, F., & Jensen, C. S. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2601611 · Cited by 37
Self-citationGhasemi, F., Fassihi, A., Pérez‐Sánchez, H., & Mehridehnavi, A. (2016) · Journal of Computational Chemistry · doi:10.1002/jcc.24671 · Cited by 35
Dr.VAE: Drug Response Variational AutoencoderPreprint
Rampášek, L., Hidru, D., Smirnov, P., Haibe‐Kains, B., & Goldenberg, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.08203 · Cited by 35
Dynamic collective routing using crowdsourcing data
Liu, S., & Qu, Q. (2016) · Transportation Research Part B Methodological · doi:10.1016/j.trb.2016.08.005 · Cited by 33
Self-citationAutomatic integration of drug indications from multiple health resources
Névéol, A., & Lu, Z. (2010) · International Health Informatics Symposium · doi:10.1145/1882992.1883096 · Cited by 30
Integrating non-spatial preferences into spatial location queries
Qu, Q., Liu, S., Yang, B., & Jensen, C. S. (2014) · International Conference on Scientific and Statistical Database Management · doi:10.1145/2618243.2618247 · Cited by 28
Self-citationEfficient Top-k Spatial Locality Search for Co-located Spatial Web Objects
Qu, Q., Liu, S., Yang, B., & Jensen, C. S. (2014) · IEEE International Conference on Mobile Data Management (MDM) · doi:10.1109/mdm.2014.39 · Cited by 26
Self-citationProtecting Anonymity in Data-Driven Biomedical Science
Kieseberg, P., Hobel, H., Schrittwieser, S., Weippl, E. R., & Holzinger, A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-43968-5_17 · Cited by 22
An Effective Privacy Architecture to Preserve User Trajectories in Reward-Based LBS Applications
Hasan, A. S. M. T., Qu, Q., Li, C., Chen, L., & Jiang, Q. (2018) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi7020053 · Cited by 22
Self-citationIntegrating domain knowledge in supervised machine learning to assess the risk of breast cancer
Bochare, A., Gangopadhyay, A., Yesha, Y., Joshi, A., Yesha, Y., Brady, M. C., Grasso, M., & Rishe, N. D. (2014) · International Journal of Medical Engineering and Informatics · doi:10.1504/ijmei.2014.060245 · Cited by 19
SUBIC: A Supervised Bi-Clustering Approach for Precision Medicine
Nezhad, M. Z., Zhu, D., Sadati, N., Yang, K., & Levi, P. (2017) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2017.00-68 · Cited by 14
Medical image super-resolution with non-local embedding sparse representation and improved IBP
Zhang, M., Desrosiers, C., Qu, Q., Guo, F., & Zhang, C. (2016) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2016.7471803 · Cited by 13
Self-citationAugmented Outcome-weighted Learning for Optimal Treatment RegimesPreprint
Zhou, X., & Kosorok, M. R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.10654 · Cited by 9
Causal nearest neighbor rules for optimal treatment regimesPreprint
Zhou, X., & Kosorok, M. R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.08451 · Cited by 9
Nezhad, M. Z., Zhu, D., Sadati, N., & Yang, K. (2018) · arXiv (Cornell University) · Cited by 7
A Noise-Filtering Approach for Cancer Drug Sensitivity PredictionPreprint
Turki, T., & Wei, Z. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.00525 · Cited by 3
Flagged first, then by citations
4 of the 146 references couldn’t be matched to a known paper.
Research works printed but not linked: 4
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Cited by
Sorted by citations
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
Medical deep learning—A systematic meta-review
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
Strianese, O., Rizzo, F. M., Ciccarelli, M., Galasso, G., D’Agostino, Y., Salvati, A., Giudice, C. D., Tesorio, P., & Rusciano, M. R. (2020) · Genes · doi:10.3390/genes11070747 · Cited by 269
Multi-Disease Prediction Based on Deep Learning: A Survey
Xie, S., Yu, Z., & Lv, Z. (2021) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2021.016728 · Cited by 243
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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