Cited by 207Pharmacogenetics and Drug MetabolismComputational Drug Discovery MethodsGenetic Associations and Epidemiology

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Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification

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Kalinin, Alexandr A, Higgins, Gerald A, Reamaroon, Narathip, Soroushmehr, Sayedmohammadreza, Allyn-Feuer, Ari, Dinov, Ivo D, Najarian, Kayvan, Athey, Brian D·2018·Pharmacogenomics

DOI 10.2217/pgs-2018-0008PMC PMC6022084

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Abstract

This Perspective provides examples of current and future applications of deep learning in pharmacogenomics, including: identification of novel regulatory variants located in noncoding domains of the genome and their function as applied to pharmacoepigenomics; patient stratification from medical records; and the mechanistic prediction of drug response, targets and their interactions. Deep learning encapsulates a family of machine learning algorithms that has transformed many important subfields of artificial intelligence over the last decade, and has demonstrated breakthrough performance improvements on a wide range of tasks in biomedicine. We anticipate that in the future, deep learning will be widely used to predict personalized drug response and optimize medication selection and dosing, using knowledge extracted from large and complex molecular, epidemiological, clinical and demographic datasets.

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Kalinin AA , PalanimalaiS , DinovID . SOCRAT platform design: a web architecture for interactive visual analytics applications . In : Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics . ACM New York , NY, USA , 1 – 6 ( 2017 ).
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Additional file 8 of Deep learning algorithm reveals two prognostic subtypes in patients with gliomas
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Additional file 1 of Deep learning algorithm reveals two prognostic subtypes in patients with gliomas
Tian, J., Zhu, M., Ren, Z., Zhao, Q., Wang, P., He, C. K., Zhang, M., Peng, X., Wu, B., Feng, R., et al. (2022) · Figshare · doi:10.6084/m9.figshare.21314163
Additional file 13 of Deep learning algorithm reveals two prognostic subtypes in patients with gliomas
Tian, J., Zhu, M., Ren, Z., Zhao, Q., Wang, P., He, C. K., Zhang, M., Peng, X., Wu, B., Feng, R., et al. (2022) · Figshare · doi:10.6084/m9.figshare.21314154
Additional file 3 of Pharmacovariome scanning using whole pharmacogene resequencing coupled with deep computational analysis and machine learning for clinical pharmacogenomics
Tafazoli, A., Mikros, J., Khaghani, F., Alimardani, M., Rafigh, M., Hemmati, M., Siamoglou, S., Golińska, A. K., Kamiński, K. A., Niemira, M., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26604139
Additional file 1 of neoDL: a novel neoantigen intrinsic feature-based deep learning model identifies IDH wild-type glioblastomas with the longest survival
Sun, T., He, Y., Li, W., Liu, G., Li, L., Wang, L., Xiao, Z., Han, X., Wen, H., Liu, Y., et al. (2021) · Figshare · doi:10.6084/m9.figshare.15047902
Additional file 2 of Deep learning algorithm reveals two prognostic subtypes in patients with gliomas
Tian, J., Zhu, M., Ren, Z., Zhao, Q., Wang, P., He, C. K., Zhang, M., Peng, X., Wu, B., Feng, R., et al. (2022) · Figshare · doi:10.6084/m9.figshare.21314166
Additional file 4 of Deep learning algorithm reveals two prognostic subtypes in patients with gliomas
Tian, J., Zhu, M., Ren, Z., Zhao, Q., Wang, P., He, C. K., Zhang, M., Peng, X., Wu, B., Feng, R., et al. (2022) · Figshare · doi:10.6084/m9.figshare.21314172
Additional file 10 of Deep learning algorithm reveals two prognostic subtypes in patients with gliomas
Tian, J., Zhu, M., Ren, Z., Zhao, Q., Wang, P., He, C. K., Zhang, M., Peng, X., Wu, B., Feng, R., et al. (2022) · Figshare · doi:10.6084/m9.figshare.21314145
Additional file 11 of Deep learning algorithm reveals two prognostic subtypes in patients with gliomas
Tian, J., Zhu, M., Ren, Z., Zhao, Q., Wang, P., He, C. K., Zhang, M., Peng, X., Wu, B., Feng, R., et al. (2022) · Figshare · doi:10.6084/m9.figshare.21314148
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Luo, X., Chen, F., Chen, Y., & Zhou, Q. (2025) · Elsevier eBooks · doi:10.1016/b978-0-44-332908-1.00032-5
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Kumar, V., Shukla, R., Chaudhary, A., & Gautam, M. (2026) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2025.1749396
Artificial Intelligence-Driven Genomic Prediction Models for Early Detection and Risk Stratification of Complex Diseases
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Study protocol for an observational trial: Investigating pharmacogenetic impact on depression treatment using various sequencing and array methods (PharmGen-TRD Study)Preprint
Darm, P., Scholl, C., Paulus, E., Reif-Leonhard, C., Glaessner, A., Qubad, M., Bloemendaal, M., Steffens, M., Oppermann, S., Reif, A., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-9017719/v1
An Overview of Role of Machine Learning in Drug Discovery
Mahalingaswamy, P., Ranganathan, V., & Chinnappan, S. (2026) · Research Journal of Pharmacy and Technology · doi:10.52711/0974-360x.2026.00203

Authors: Kalinin, A. A., Higgins, G. A., Reamaroon, N., Soroushmehr, S., Allyn-Feuer, A., Dinov, I. D., Najarian, K., & Athey, B. D.
Affiliations: University of Michigan (US)
Corresponding author: Athey, Brian D
ORCID: Higgins, Gerald A
Dinov, Ivo D
Venue: Pharmacogenomics
Publisher: Informa UK Limited
Published: 2018-04-26
Volume: 19
Issue: 7
Pages: 629-650
ISSN: 1462-2416 (ISSN-L)
1744-8042
DOI: 10.2217/pgs-2018-0008
PMC: PMC6022084
OpenAlex: W2784341656
Cited by: 207
References: 120
Citations by year: 2026: 13 · 2025: 26 · 2024: 23 · 2023: 21 · 2022: 35 · 2021: 36 · 2020: 21 · 2019: 17 · 2018: 13
FWCI: 4.22
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: Pharmacogenomics (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: National Institutes of Health (P20 NR015331, U54 EB020406, P50 NS091856, P30 DK089503, P30 AG053760, T32 GM070449)
National Science Foundation (1734853, 1636840, 1416953, 0716055, 1023115)
Elsie Andresen Fiske Research Fund
Topics: Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacogenetics and Drug Metabolism
Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Keywords: deep learning, gene expression regulation, regulatory variants, non-coding genome, pharmacoepigenomics, patient stratification, electronic health records, drug response prediction, drug-target interactions
MeSH terms: Deep Learning, Algorithms, Humans, Pharmacogenetics, Neural Networks, Computer, Models, Educational, Databases as Topic

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Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification

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