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Supervised Machine Learning for Population Genetics: A New Paradigm
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Abstract
As population genomic datasets grow in size, researchers are faced with the daunting task of making sense of a flood of information. To keep pace with this explosion of data, computational methodologies for population genetic inference are rapidly being developed to best utilize genomic sequence data. In this review we discuss a new paradigm that has emerged in computational population genomics: that of supervised machine learning (ML). We review the fundamentals of ML, discuss recent applications of supervised ML to population genetics that outperform competing methods, and describe promising future directions in this area. Ultimately, we argue that supervised ML is an important and underutilized tool that has considerable potential for the world of evolutionary genomics.
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MethodA Machine Learning Method for Detecting Autocorrelation of Evolutionary Rates in Large Phylogenies
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Distinguishing between recent balancing selection and incomplete sweep using deep neural networks
Işıldak, U., Stella, A., & Fumagalli, M. (2021) · Molecular Ecology Resources · doi:10.1111/1755-0998.13379 · Cited by 52
“A promising solution to address this issue is provided by super - vised machine learning (ML) which has been recently introduced in population genetics and successfully applied for evolutionary inferences (Schrider & Kern, 2018).”
Multispecies hybridization in birds
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Multiple-kernel learning for genomic data mining and prediction
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Ancient RNA virus epidemics through the lens of recent adaptation in human genomes
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“…in this respect, because we now have good genome datasets (Genomes Project et al., 2015) and statistical tools (Ferrer-Admetlla et al., 2014; Schrider and Kern, 2016, 2017, 2018; Voight et al., 2006) to detect signals of recent adaptation genome-wide in the form of recent selective sweeps.”
Applications of Machine Learning in Phylogenetics
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An overview of Machine Learning Technologies and their use in E-learning
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“Random forest approaches can be used within ABC frameworks and significantly reduce computing effort (Pudlo et al. 2016; Schrider and Kern 2018).”
Orthodontics in the era of big data analytics
Allareddy, V., Venugopalan, S. R., Nalliah, R. P., Caplin, J., Lee, M. K., & Allareddy, V. (2019) · Orthodontics and Craniofacial Research · doi:10.1111/ocr.12279 · Cited by 54
“29 Furthermore, high-throughput methods such as GWAS, transcriptome, proteome and epigenome provide large volumes of data in a unidimensional view.”
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Discovery of Ongoing Selective Sweeps within Anopheles Mosquito Populations Using Deep Learning
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Lavallin, A. V., & Downs, J. A. (2021) · Geography Compass · doi:10.1111/gec3.12563 · Cited by 61
Using Supervised Learning Methods for Gene Selection in RNA-Seq Case-Control Studies
Wenric, S., & Shemirani, R. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00297 · Cited by 48
“…of using importance measures generated by the random forests algorithm to extract a ranking of features has already been explored with several different data sets, although, to our knowledge, this has never been done with RNA-Seq data sets [Schrider2018, Freres2016, Yao2015, Duro2012, Anaissi2013].”
MethodExpanding the stdpopsim species catalog, and lessons learned for realistic genome simulations
Lauterbur, M. E., Cavassim, M. I. A., Gladstein, A. L., Gower, G., Pope, N. S., Tsambos, G., Adrion, J. R., Belsare, S., Biddanda, A., Caudill, V. R., et al. (2023) · eLife · doi:10.7554/elife.84874 · Cited by 67
A statistical model for reference-free inference of archaic local ancestry
Durvasula, A., & Sankararaman, S. (2019) · PLoS Genetics · doi:10.1371/journal.pgen.1008175 · Cited by 44
Inheritance of Rootstock Effects in Avocado (Persea americana Mill.) cv. Hass
Reyes-Herrera, P. H., Muñoz‐Baena, L., Velásquez‐Zapata, V., C., L. P. P., Delgado-Paz, O. A., Diez, C. A. D., Arboleda, A. A. N., & Cortés, A. J. (2020) · Frontiers in Plant Science · doi:10.3389/fpls.2020.555071 · Cited by 47
Bourgeois, Y., & Boissinot, S. (2019) · Molecular Ecology · doi:10.1111/mec.15162 · Cited by 43
“However, machine learning is less sensitive to the choice of summary statistics and requires less simulations (Schrider & Kern, 2018).”
BackgroundInfluentialIntegrating Networks, Phylogenomics, and Population Genomics for the Study of Polyploidy
Blischak, P. D., Mabry, M. E., Conant, G. C., & Pires, J. C. (2018) · Annual Review of Ecology Evolution and Systematics · doi:10.1146/annurev-ecolsys-121415-032302 · Cited by 41
Machine learning technology in the application of genome analysis: A systematic review
Wu, J., & Zhao, Y. (2019) · Gene · doi:10.1016/j.gene.2019.04.062 · Cited by 47
Tapping Diversity From the Wild: From Sampling to Implementation
Hübner, S., & Kantar, M. B. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2021.626565 · Cited by 43
Challenges in developing cell culture media using machine learning
Hashizume, T., & Ying, B. (2023) · Biotechnology Advances · doi:10.1016/j.biotechadv.2023.108293 · Cited by 47
Statistical and Machine-Learning Analyses in Nutritional Genomics Studies
Khorraminezhad, L., Leclercq, M., Droit, A., Bilodeau, J., & Rudkowska, I. (2020) · Nutrients · doi:10.3390/nu12103140 · Cited by 43
“In addition, unsupervised ML needs an external evaluation to be sure that the results are meaningful [57].”
BackgroundQi, X., An, H., Hall, T. E., Di, C., Blischak, P. D., McKibben, M. T. W., Hao, Y., Conant, G. C., Pires, J. C., & Barker, M. S. (2021) · New Phytologist · doi:10.1111/nph.17194 · Cited by 43
Albahri, A. S., Hamid, R. A., Zaidan, A. A., & Albahri, O. S. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07822-0 · Cited by 43
“Furthermore, details about these algorithms and their importance are described in [75–80].”
BackgroundAlbahri, A. S., Zaidan, A. A., Alsattar, H. A., Hamid, R. A., Albahri, O. S., Qahtan, S., & Alamoodi, A. H. (2022) · Computational Intelligence · doi:10.1111/coin.12562 · Cited by 44
Dispersal inference from population genetic variation using a convolutional neural network
Smith, C. C. R., Tittes, S. B., Ralph, P. L., & Kern, A. D. (2023) · Genetics · doi:10.1093/genetics/iyad068 · Cited by 42
Gaffoor, Z., Pietersen, K., Jovanovic, N. Z., Bagula, A., & Kanyerere, T. (2020) · Water · doi:10.3390/w12102796 · Cited by 42
“Supervised machine-learning algorithms primarily perform either regression (e.g., linear regression) or classification (e.g., k-nearest neighbor) [76].”
MethodPredicting the decision making chemicals used for bacterial growth
Ashino, K., Sugano, K., Amagasa, T., & Ying, B. (2019) · Scientific Reports · doi:10.1038/s41598-019-43587-8 · Cited by 40
Vaughn, A. H., & Nielsen, R. (2024) · Molecular Biology and Evolution · doi:10.1093/molbev/msae156 · Cited by 40
“A variety of machine learning methods to detect selection have additionally been developed (Schrider and Kern 2018; Torada et al. 2019; Hejase et al. 2021), which all rely on training models on a set of features obtained from sequence data.”
Mo, Z., & Siepel, A. C. (2023) · PLoS Genetics · doi:10.1371/journal.pgen.1011032 · Cited by 43
“The facility of generating synthetic training data serves as the foundation of the new simulate-and-train paradigm of supervised machine learning for population genetics inference ( Fig 1A; [7,13]).”
Ding, M., Wang, T., Ko, A. M., Chen, H., Wang, H., Dong, G., Lü, H., He, W., Wangdue, S., Yuan, H., et al. (2020) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2019.2968 · Cited by 39
Genome-Wide Association Study Statistical Models: A Review
Yoosefzadeh-Najafabadi, M., Eskandari, M., Belzile, F. J., & Torkamaneh, D. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2237-7_4 · Cited by 40
Zhang, X., Kim, B., Singh, A., Sankararaman, S., Durvasula, A., & Lohmueller, K. E. (2023) · Molecular Biology and Evolution · doi:10.1093/molbev/msad001 · Cited by 29
Thompson, A., Liebeskind, B. J., Scully, E. J., & Landis, M. J. (2024) · Systematic Biology · doi:10.1093/sysbio/syad074 · Cited by 37
Distinct Patterns of Selective Sweep and Polygenic Adaptation in Evolve and Resequence Studies
Barghi, N., & Schlötterer, C. (2020) · Genome Biology and Evolution · doi:10.1093/gbe/evaa073 · Cited by 39
“Machine learning has been used for identification of loci under hard sweep, soft sweep, and balancing selection (Lin et al. 2011; Schrider and Kern 2016, 2018; Sheehan and Song 2016; Kern and Schrider 2018).”
MethodIntelligent and trusted metaheuristic optimization model for reliable agricultural network
Rehman, A. U., Abunadi, I., Haseeb, K., Saba, T., & Lloret, J. (2023) · Computer Standards & Interfaces · doi:10.1016/j.csi.2023.103768 · Cited by 37
Metadata
- Authors
- Schrider, D. R., & Kern, A. D.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- Corresponding author
- Schrider, Daniel R.
- Kern, Andrew D.
- Venue
- Trends in Genetics
- Publisher
- Elsevier BV
- Published
- 2018-04
- Volume
- 34
- Issue
- 4
- Pages
- 301-312
- ISSN
- 0168-9525 (ISSN-L)
- PMC
- PMC5905713
- OpenAlex
- W2783880833
- Cited by
- 453
- References
- 110
- Citations by year
- 2026: 41 · 2025: 55 · 2024: 51 · 2023: 50 · 2022: 68 · 2021: 65 · 2020: 57 · 2019: 46 · 2018: 20
- FWCI
- 6.48
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Trends in Genetics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion, cc-by)
- PubMed Central (acceptedVersion, cc-by)
- Funders
- National Institutes of Health
- National Human Genome Research Institute (K99HG008696)
- National Institute of General Medical Sciences (R01GM117241)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
- Keywords
- supervised learning, population genetics, population genetic inference, genome sequencing data, evolutionary genomics
- MeSH terms
- Supervised Machine Learning, Biological Evolution, Genetics, Population, Humans, Selection, Genetic, Genome, Human, Data Mining, Datasets as Topic
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