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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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BackgroundMethodApplying machine learning to predict reproductive condition in fish
Flores, A., Wiff, R., Donovan, C., & Gálvez, P. (2024) · Ecological Informatics · doi:10.1016/j.ecoinf.2024.102481 · Cited by 33
The genetic architecture of primary biliary cholangitis
Gerussi, A., Carbone, M., Corpechot, C., Schramm, C., Asselta, R., & Invernizzi, P. (2021) · European Journal of Medical Genetics · doi:10.1016/j.ejmg.2021.104292 · Cited by 31
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Haplotype and population structure inference using neural networks in whole-genome sequencing dataPreprint
Meisner, J., & Albrechtsen, A. (2022) · Genome Research · doi:10.1101/gr.276813.122 · Cited by 33
“Most studies in population genetics using NNs for parameter inference have mainly focused on supervised learning through simulations from demographic models (Sheehan and Song 2016; Chan et al. 2018; Schrider and Kern 2018; Flagel et al. 2019; Goweret al. 2021).”
Provost, K. L., Myers, E. A., & Smith, B. T. (2021) · Journal of Biogeography · doi:10.1111/jbi.14115 · Cited by 30
“…type of machine learning that is particularly powerful, but underused, in evolutionary biology is supervised machine learning because it can incorporate what is already known about the data when making inferences and allows for the modeling of many alternative simulations (Schrider & Kern, 2018).”
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Detecting Positive Selection in Populations Using Genetic Data
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The demography of extinction in eastern North American birds
Smith, B. T., Gehara, M., & Harvey, M. G. (2021) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2020.1945 · Cited by 31
“SML is statistically robust and outperforms traditional methods [50].”
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da Fonseca, E. M., Colli, G. R., Werneck, F. P., & Carstens, B. C. (2021) · Molecular Ecology Resources · doi:10.1111/1755-0998.13427 · Cited by 29
“…used in fields such as medical sciences (Mobadersany et al., 2018) and agriculture (Kamilaris & Prenafeta-Boldú, 2018), but their usage in evolutionary biology has just begun (see Blischak et al., 2020; Flagel et al., 2019; Sanchez et al., 2020; Schrider & Kern, 2018; Torada et al., 2019).”
MethodResultVersatile Detection of Diverse Selective Sweeps with Flex-Sweep
Lauterbur, M. E., Munch, K., & Enard, D. (2023) · Molecular Biology and Evolution · doi:10.1093/molbev/msad139 · Cited by 29
“Combining multiple statistics in machine learning (Pybus et al. 2015; Kern and Schrider 2018; Mughal and DeGiorgio 2018; Flagel et al. 2019; Mughal et al. 2020), Approximate Bayesian Computation (Racimo et al. 2014), or composite of multiple signals (Grossman et al. 2010; Sugden et al. 2018)…”
BackgroundMethodResultInfluentialData-driven approaches to improve water-use efficiency and drought resistance in crop plants
Sharma, N., Raman, H., Wheeler, D. A., Yogendra, K. N., & Sharma, R. A. (2023) · Plant Science · doi:10.1016/j.plantsci.2023.111852 · Cited by 28
An ABC Method for Whole-Genome Sequence Data: Inferring Paleolithic and Neolithic Human Expansions
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Lajaaiti, I., Lambert, S., Voznica, J., Morlon, H., & Hartig, F. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.03.530992 · Cited by 25
Uncertainty reduction in biochemical kinetic models: Enforcing desired model properties
Mišković, L., Béal, J., Moret, M., & Vassily Hatzimanikatis, A. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007242 · Cited by 29
“Machine learning methods (38-42) have found applications in a large number of biological and biomedical areas such as cancer research (43-45), population genetics (46, 47), protein structure and function prediction and phylogenomic mapping (48-52), protein-protein interactions (53-55), medical imaging (56-60), gene ex…”
BackgroundA Deep Learning Approach for Detecting Copy Number Variation in Next-Generation Sequencing Data
Hill, T., & Unckless, R. L. (2019) · G3 Genes Genomes Genetics · doi:10.1534/g3.119.400596 · Cited by 27
“…population genomics (Larrañaga et al. 2006; Pedregosa et al. 2011; Libbrecht and Noble 2015; Schrider and Kern 2016; Sheehan and Song 2016; Kern and Schrider 2018; SchriderandKern 2018),wesought todevelop a method to accurately and quickly classify the presence or absence of copy number variants…”
BackgroundMethodSimulation of bacterial populations with SLiM
Cury, J., Haller, B. C., Achaz, G., & Jay, F. (2022) · Peer Community Journal · doi:10.24072/pcjournal.72 · Cited by 27
Cheng, B., Zhou, P., & Chen, Y. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04805-9 · Cited by 27
“With the rapid growth of genetic information in recent years, machine learning has turned into an extremely important research tool because it can find complicated patterns in high-dimensional data to achieve various purposes [30].”
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Unveiling recent and ongoing adaptive selection in human populations
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Deep learning and satellite imagery predict genetic diversity and differentiation
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“It has been used successfully in population genetics to identify selective sweeps, inferring demographic history and recombination rates (Schrider & Kern, 2018; Sheehan & Song, 2016), and also in meteorology/oceanography to predict the occurrence of ENSO events (Ham et al., 2019).”
BackgroundMethodThe multiple uses of artificial intelligence in exercise programs: a narrative review
Canzone, A., Belmonte, G., Patti, A., Vicari, D. S. S., Fabio, R., Giustino, V., Drid, P., & Bianco, A. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1510801 · Cited by 34
Applying Stack Bidirectional LSTM Model to Intrusion Detection
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“Among them, it can be divided into two categories: “supervised learning” and “unsupervised learning” [Schrider and Kern (2018)].”
BackgroundMethodIntroUNET: Identifying introgressed alleles via semantic segmentation
Ray, D. D., Flagel, L., & Schrider, D. R. (2024) · PLoS Genetics · doi:10.1371/journal.pgen.1010657 · Cited by 23
Predicting the geographic origin of Spanish Cedar (Cedrela odorata L.) based on DNA variation
Finch, K. N., Cronn, R., Richter, M. C. A., Blanc-Jolivet, C., Guerrero, M. C. C., Destéfano‐Beltrán, L., GARCÍA-DÁVILA, C. R., Coronado, E. N. H., Palacios‐Ramos, S., Paredes-Villanueva, K., et al. (2020) · Conservation Genetics · doi:10.1007/s10592-020-01282-6 · Cited by 23
Distinguishing Felsenstein Zone from Farris Zone Using Neural Networks
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“…on a rejection step that discards most of the simulations and leaves only a small proportion that are more similar to the empirical data (Csilléry et al., 2012), the CNN uses information from the whole set of simulations to learn how to distinguish the concurrent scenarios (Schrider & Kern, 2018).”
MethodBooker, W. W., Ray, D. D., & Schrider, D. R. (2023) · GENETICS · doi:10.1093/genetics/iyad063 · Cited by 22
Comparative phylogeographic inference with genome‐wide data from aggregated population pairs
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DeepDive: estimating global biodiversity patterns through time using deep learning
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“These have been used to estimate age-dependent extinction 73 , population genomics parameters 74,75 and birth-death rates in a phylogenetic context 76 .”
Integrative genomics would strengthen AMR understanding through ONE health approach
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Abiotic Stress Tolerance Boosted by Genetic Diversity in Plants
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“Interest-ingly, modern machine learning (ML) resources [72,73] provide transversal perspectives for more efficient mining [74], mapping [75], and validation [76] (step 1–3) of pre-existent and novel candidate gene variants [77].”
BackgroundAn Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis
Santucci, V., Milani, A., & Caraffini, F. (2019) · Mathematics · doi:10.3390/math7111051 · Cited by 19
“Successful studies using supervised classification strategies, for example., cardiovascular medicine [17] and genetics [18], are present in the literature.”
MethodSaha, S., & Narayanan, S. (2022) · World Development · doi:10.1016/j.worlddev.2022.105860 · Cited by 20
“Most importantly machine learning allows the use data as they are “in nature, rather than in a way we represent it in a model”(Schrider and Kern, 2018).”
BackgroundChromosome‐scale inference of hybrid speciation and admixture with convolutional neural networks
Blischak, P. D., Barker, M. S., & Gutenkunst, R. N. (2021) · Molecular Ecology Resources · doi:10.1111/1755-0998.13355 · Cited by 24
“…data (e.g. msprime and SLiM 3; Haller & Messer, 2019; Kelleher et al. 2016) provide opportunities to explore likelihood-free inference methods such as approximate Bayesian computation (ABC; Beaumont et al. 2002) and supervised machine learning (Schrider & Kern, 2018; Sheehan & Song, 2016).”
MethodShaik, S., Singh, A., Suresh, A., & Ahmed, N. (2022) · mBio · doi:10.1128/mbio.03796-21 · Cited by 22
“Machine learning algorithms help not only in classi fi cation or regression-related objectives but also in the selection of important relevant features for inference (28).”
BackgroundMethodGovender, P., Fashoto, S. G., Maharaj, L., Adeleke, M. A., Mbunge, E., Olamijuwon, J. O., Akinnuwesi, B. A., & Okpeku, M. (2022) · PLoS ONE · doi:10.1371/journal.pone.0263790 · Cited by 20
“Advances in genomic data reveal that early applications of ML for genetic inferences demonstrated that they outperform traditional approaches such as AMOVA [38].”
BackgroundMethodResultInfluentialA deep learning framework for characterization of genotype data
Ausmees, K., & Nettelblad, C. (2022) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkac020 · Cited by 21
“For many applications dealing with complex and heterogeneous information, the data-driven approach has become a promising alternative or complement to more traditional model-6 based methods (Xu and Jackson 2019; Libbrecht and Noble 2015; Schrider and Kern 2018).”
BackgroundSelection leads to false inferences of introgression using popular methods
Smith, M. L., & Hahn, M. W. (2024) · Genetics · doi:10.1093/genetics/iyae089 · Cited by 22
Uncovering Footprints of Natural Selection Through Spectral Analysis of Genomic Summary Statistics
Arnab, S. P., Amin, M. R., & DeGiorgio, M. (2023) · Molecular Biology and Evolution · doi:10.1093/molbev/msad157 · Cited by 21
“To overcome the pitfalls associated with using a single summary statistic to uncover signals of evolutionary processes, combining the knowledge garnered from a plethora of summary statistics has become an emerging trend (Schrider and Kern 2018).”
The role of artificial intelligence in crop improvement
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Localizing Post-Admixture Adaptive Variants with Object Detection on Ancestry-Painted Chromosomes
Hamid, I., Korunes, K. L., Schrider, D. R., & Goldberg, A. (2023) · Molecular Biology and Evolution · doi:10.1093/molbev/msad074 · Cited by 19
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Predicting Case Fatality of Dengue Epidemic: Statistical Machine Learning Towards a Virtual Doctor
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Genetic and Genomic Resources for Soybean Breeding Research
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“Furthermore, the availability of genomic datasets and connected phenotypic and marker databases also builds a foundation for next-generation breeding technologies, such as genomic prediction [122], genome-wide scans of selection signatures [123], machine learning [124] and speed breeding [125].”
BackgroundHaleem, A., Klees, S., Schmitt, A. O., & Gültaş, M. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23095121 · Cited by 17
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The Tree Reconstruction Game: Phylogenetic Reconstruction Using Reinforcement Learning
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“Machine learning has been applied to multiple tasks in biology, including molecular biology, evolutionary, and ecology research (Tarca et al. 2007; Schrider and Kern 2018; Suvorov et al. 2020; Zou et al. 2020; Azouri et al. 2021; Haag et al. 2022; Zaharias et al. 2022).”
Klingström, T., König, E. Z., & Zwane, A. A. (2024) · Briefings in Functional Genomics · doi:10.1093/bfgp/elae032 · Cited by 19
“Machine Learning methods are widely used in genetics [79] and described further in the phenotyping methods section of this review.”
Unifying Theoretical and Empirical Perspectives on Genomic Differentiation
Semenov, G. А., Safran, R. J., Smith, C. C. R., Turbek, S. P., Mullen, S. P., & Flaxman, S. M. (2019) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2019.07.008 · Cited by 17
“Recent developments in supervised machine learning show great promise in overcoming these challenges due to their ability to accommodate complex patterns and search for signals of local adaptation and reproductive isolation while accounting for other factors, such as nonequilibrium demography and recombination rates […”
BackgroundVarious Aspects of Sentiment Analysis: A ReviewPreprint
Pandey, H., Mishra, A. K., & Kumar, N. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3349011 · Cited by 17
Machine learning approaches to the human metabolome in sepsis identify metabolic links with survival
Kosyakovsky, L. B., Somerset, E., Rogers, A. J., Sklar, M. C., Mayers, J. R., Toma, A., Szekely, Y., Soussi, S., Wang, B., Fan, C. S., et al. (2022) · Intensive Care Medicine Experimental · doi:10.1186/s40635-022-00445-8 · Cited by 16
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“If by some stroke of good luck one affiliation rule calculation is utilized and there are numerous information to be investigated, the execution season of the calculation will turn out to be extremely lengthy [21].”
Rákosy, D., Ashman, T., Zoller, L., Stanley, A., & Knight, T. M. (2022) · Functional Ecology · doi:10.1111/1365-2435.14211 · Cited by 16
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“Supervised learning, a type of ML in which data are used to train a model to predict outcomes from large datasets (Soltis et al., 2020), has great potential to answer evolutionary genomics questions (Schrider and Kern, 2018).”
BackgroundCecil, R. M., & Sugden, L. A. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010979 · Cited by 19
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Intelligence clinique et intelligence artificielle
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A deep learning approach for staging embryonic tissue isolates with small data
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“A few other notable examples include the wide use of ML approaches to address questions in population and evolutionary genetics [14], predicting transcript abundance from DNA sequence [15], predicting methylation states in single cells [16], imputation of missing SNPs [17] as well as variant calling [18].”
BackgroundOvercast, I., Achaz, G., Aguilée, R., Andújar, C., Arribas, P., Creedy, T. J., Economo, E. P., Etienne, R. S., Gillespie, R. G., Jacquet, C., et al. (2022) · Global Ecology and Biogeography · doi:10.1111/geb.13604 · Cited by 20
“Machine learning (ML) methods offer a likelihood-free approach and are gaining significant attention for application to ecological and evolutionary questions (Schrider & Kern, 2018 ; Sheehan & Song, 2016).”
Background‘Omics’ Approaches for Crop Improvement
Cortés, A. J., Castillejo, M. Á., & Yockteng, R. (2023) · Agronomy · doi:10.3390/agronomy13051401 · Cited by 16
A Novel Ensemble Stacking Classification of Genetic Variations Using Machine Learning Algorithms
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Farrell, E. D., Andersson, L., Bekkevold, D., Campbell, N., Carlsson, J., Clarke, M., Egan, A., Folkvord, A., Gras, M., Lusseau, S. M., et al. (2022) · Royal Society Open Science · doi:10.1098/rsos.220453 · Cited by 15
“SML is concerned with predicting the value of a response label/category on the basis of the input variables/features [32].”
BackgroundModeling Substitution Rate Evolution across Lineages and Relaxing the Molecular Clock
Mello, B., & Schrago, C. G. (2024) · Genome Biology and Evolution · doi:10.1093/gbe/evae199 · Cited by 16
“Recent applications of several machine learning algorithms to evolutionary biology have opened up promising avenues for exploring models of rate evolution (Schrider and Kern 2018; Schrago and Mello 2020; Azouri et al. 2021).”
BackgroundTree Sequences as a General-Purpose Tool for Population Genetic Inference
Whitehouse, L. S., Ray, D. D., & Schrider, D. R. (2024) · Molecular Biology and Evolution · doi:10.1093/molbev/msae223 · Cited by 16
“…(Hochreiter and Schmidhuber 1997; LeCun et al. 1998; Erhan et al. 2013; Gu et al. 2018; Li et al. 2022) and, in recent years, have been successfully applied to a number of problems in population genetics (Sheehan and Song 2016; Schrider and Kern 2018; Korfmann, Gaggiotti, and Fumagalli 2023 ).”
InfluentialIdentification of transcriptome-wide, nut weight-associated SNPs in Castanea crenata
Kang, M., Shin, A.-Y., Shin, Y., Lee, S., Lee, H.-R., Kim, T., Choi, M., Koo, N., Kim, Y., Kyeong, D., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-49618-8 · Cited by 15
de Oliveira, E. A., Perez, M. F., Bertollo, L. A. C., Gestich, C. C., Ráb, P., Ezaz, T., de Souza, F. H. S., Viana, P. F., Feldberg, E., de Oliveira, E. H. C., et al. (2020) · Ecography · doi:10.1111/ecog.04874 · Cited by 15
Evolutionary Genetics of Crop-Wild Complexes
Cortés, A. J., Cornille, A., & Yockteng, R. (2021) · Genes · doi:10.3390/genes13010001 · Cited by 15
“Based on this compilation, we encourage oncoming studies to: (1) explicitly test evolutionary scenarios concerning the domestication of the ancient grain amaranth via approximate Bayesian computation (ABC) demographic inferences [13] and ML [19,20], (2) narrow the genetic mapping of key domestication-related traits in…”
MethodMachine-Learning Prospects for Detecting Selection Signatures Using Population Genomics Data
Kumar, H., Panigrahi, M., Panwar, A., Rajawat, D., Nayak, S. S., Saravanan, K. A., Kaisa, K., Parida, S., Bhushan, B., & Dutt, T. (2022) · Journal of Computational Biology · doi:10.1089/cmb.2021.0447 · Cited by 18
“Schrider and Kern (2018) introduced ML for the identification of genomic regions under selection, though not entirely new in population genetics.”
Deciphering signatures of natural selection via deep learning
Qin, X., Chiang, C. W. K., & Gaggiotti, O. E. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac354 · Cited by 17
“Therefore, it introduces the same model mis-specification issues that affect statistical inference based on generative models [70].”
BackgroundEfficient Detection and Characterization of Targets of Natural Selection Using Transfer Learning
Arnab, S. P., dos Santos, A. L. C., Fumagalli, M., & DeGiorgio, M. (2025) · Molecular Biology and Evolution · doi:10.1093/molbev/msaf094 · Cited by 14
“Together, these components offer a comprehensive view of the effects of selective sweeps on genomic regions (Schrider and Kern 2016; Kern and Schrider 2018 ).”
InfluentialThe impacts of fine-tuning, phylogenetic distance, and sample size on big-data bioacoustics
Provost, K. L., Yang, J., & Carstens, B. C. (2022) · PLoS ONE · doi:10.1371/journal.pone.0278522 · Cited by 14
CpG-creating mutations are costly in many human viruses
Caudill, V. R., Qin, S., Winstead, R., Kaur, J., Tisthammer, K. H., Pineda, E. G., Solis, C., Cobey, S. E., Bedford, T., Carja, O., et al. (2020) · Evolutionary Ecology · doi:10.1007/s10682-020-10039-z · Cited by 13
Yu, G.-E., Shin, Y., Subramaniyam, S., Kang, S.-H., Lee, S.-M., Cho, C., Lee, S.-S., & Kim, C. (2021) · Scientific Reports · doi:10.1038/s41598-021-87281-0 · Cited by 14
Mathematical constraints on FST: multiallelic markers in arbitrarily many populations
Alcala, N., & Rosenberg, N. (2016) · bioRxiv · doi:10.1098/rstb.2020.0414 · Cited by 13
“Further, joint use of M along with FST could be useful in various applications of FST, such as in inference of model parameters by approximate Bayesian computation [30] and machine learning [31].”
MethodChehida, Y. B., Stelwagen, T., Hoekendijk, J. P. A., Ferreira, M., Eira, C., Pereira, A., Nicolau, L., Thumloup, J., & Fontaine, M. C. (2023) · Ecology and Evolution · doi:10.1002/ece3.10819 · Cited by 15
“In addition, we computed the confusion matrix, which provides a more global assessment of the performance of our RF procedure (Schrider & Kern, 2018 ).”
MethodYang, J., Cui, Y., Yu, D., Zhang, G., Cao, R., Gu, Z., Dai, G., Wu, X., Ling, Y., Yi, C., et al. (2023) · Molecular Biology and Evolution · doi:10.1093/molbev/msad142 · Cited by 15
“…such as selective sweeps and machine learning–based methods (Kaplan et al. 1989; Kim and Stephan 2002; Li and Stephan 2005, 2006; Lin et al. 2011; Schrider and Kern 2018; Stephan 2019), are not suitable for SARS-CoV-2 and may not reliably detect advantageous mutations causing the high…”
The Use of AI for Phenotype-Genotype Mapping
Sharma, J., & Goel, P. (2025) · Methods in molecular biology · doi:10.1007/978-1-0716-4690-8_21 · Cited by 13
Estimation of demography and mutation rates from one million haploid genomes
Schraiber, J. G., Spence, J. P., & Edge, M. D. (2025) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2025.07.008 · Cited by 16
Power and limits of selection genome scans on temporal data from a selfing population
Navascués, M., Becheler, A., Gay, L., Ronfort, J., Loridon, K., & Vitalis, R. (2021) · Peer Community Journal · doi:10.24072/pcjournal.47 · Cited by 13
Prediction and classification of diabetes mellitus using genomic data
Awotunde, J. B., Ayo, F. E., Jimoh, R. G., Ogundokun, R. O., Matiluko, O. E., Oladipo, I. D., & AbdulRaheem, M. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821187-8.00009-5 · Cited by 14
SimBit: A high performance, flexible and easy‐to‐use population genetic simulator
Matthey‐Doret, R. (2021) · Molecular Ecology Resources · doi:10.1111/1755-0998.13372 · Cited by 13
Identification of natural selection in genomic data with deep convolutional neural network
Fadja, A. N., Riguzzi, F., Bertorelle, G., & Trucchi, E. (2021) · BioData Mining · doi:10.1186/s13040-021-00280-9 · Cited by 15
“For an overview on how to apply Machine Learning to genetic data see [27].”
MethodNeural networks for self-adjusting mutation rate estimation when the recombination rate is unknown
Burger, K. E., Pfaffelhuber, P., & Baumdicker, F. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010407 · Cited by 15
Strong neutral sweeps occurring during a population contraction
Moinet, A., Schlichta, F., Peischl, S., & Excoffier, L. (2022) · Genetics · doi:10.1093/genetics/iyac021 · Cited by 13
Inference of selective sweep parameters through supervised learningPreprint
Caldas, I. V., Clark, A. G., & Messer, P. W. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.19.500702 · Cited by 15
“Supervised machine learning provides a new paradigm for evolutionary analyses that has gained increasing attention over the past years (Schrider & Kern, 2018).”
BackgroundDecoding genomic landscapes of introgression
Huang, X., Hackl, J., & Kuhlwilm, M. (2025) · Trends in Genetics · doi:10.1016/j.tig.2025.07.001 · Cited by 13
Rodríguez, E., Waissman, J., Mahadevan, P., Villa-Angulo, C. E., Flores, B. L., & Villa, R. (2019) · Genetics and Molecular Research · doi:10.4238/gmr18407 · Cited by 12
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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