[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123174-en":3,"doc-seo-123174-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123174,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Domain-adaptive neural networks improve supervised machine learning based on simulated population genetic data","Population genetics is increasingly leveraging supervised machine learning trained on simulated data, enabling high-accuracy inference of evolutionary parameters. When simulations do not match real-world data, “simulation misspecification” can cause supervised models to underperform. This work reframes misspecification as a domain adaptation problem, applying a gradient reversal layer (GRL) technique to reduce distribution shift between simulated and real datasets. The analysis targets SIA and ReLERNN, showing mitigation of mis-specification effects and improved selection inference via a domain-adaptive SIA model on 1000 Genomes CEU data.","PLOS GENETICS  \nOPEN ACCESS  \nCitation: Mo Z, Siepel A (2023) Domain-adaptive neural networks improve supervised machine learning based on simulated population genetic data. PLoS Genet 19(11): e1011032 . [https://doi](https://doi). org/10 .1371/journal.pgen.1011032  \nEditor: Daniel R. Schrider, University of North Carolina, UNITED STATES  \nReceived: March 17, 2023  \nAccepted: October 23, 2023  \nPublished: November 7, 2023  \nPeer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: [https://doi.org/10.1371/journal.pgen.101](https://doi.org/10.1371/journal.pgen.101)1032  \nCopyright: © 2023 Mo, Siepel. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData Availability Statement: All code used in this [study are available at github.com/ziyimo/popgen](study are available at github.com/ziyimo/popgen)dom-adapt. The 1000 Genomes data are available [at ](at www.internationalgenome.org/data)[www.internationalgenome.org/data](at www.internationalgenome.org/data).  \nRESEARCH ARTICLE  \nDomain-adaptive neural networks improve supervised machine learning based on simulated population genetic data  \nZiyi Mo1,2, Adam Siepel1,2 *  \n1 Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America, 2 School of Biological Sciences, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America  \n* [asiepel@cshl.edu](asiepel@cshl.edu)  \nAbstract  \nInvestigators have recently introduced powerful methods for population genetic inference that rely on supervised machine learning from simulated data. Despite their performance advantages, these methods can fail when the simulated training data does not adequately resemble data from the real world. Here, we show that this “simulation mis-specification”problem can be framed as a “domain adaptation” problem, where a model learned from one data distribution is applied to a dataset drawn from a different distribution. By applying an established domain-adaptation technique based on a gradient reversal layer (GRL), originally introduced for image classification, we show that the effects of simulation mis-specification can be substantially mitigated. We focus our analysis on two state-of-the-art deeplearning population genetic methods—SIA , which infers positive selection from features of the ancestral recombination graph (ARG), and ReLERNN, which infers recombination rates from genotype matrices. In the case of SIA, the domain adaptive framework also compensates for ARG inference error. Using the domain-adaptive SIA (dadaSIA) model, we estimate improved selection coefficients at selected loci in the 1000 Genomes CEU population. We anticipate that domain adaptation will prove to be widely applicable in the growing use of supervised machine learning in population genetics.  \nAuthor summary  \nPopulation genetic simulation is a powerful tool in the study of evolution. A number of supervised machine learning methods have been developed that take advantage ofinexpensive simulations as training data. Despite their outstanding performance in benchmarks, these models can fail when the simulated training data deviate from the real data. In this work, we employed domain adaptation techniques to address this “simulation misspecification” problem by training the machine learning model jointly with simulated and real data. We performed extensive benchmark experiments to demonstrate the improvement of the domain-adaptive models over standard machine learning models in the presence of different types of mis-specification. In addition, w","cbCaisNgQ6hk04ku","https://ap.wps.com/l/cbCaisNgQ6hk04ku","pdf",2126579,1,22,"English","en",105,"# Abstract\n# Author summary\n# Introduction","[{\"question\":\"What problem does the paper address in supervised population genetic machine learning?\",\"answer\":\"It addresses failure caused by simulation misspecification, where simulated training data differs from real-world population genetic data.\"},{\"question\":\"How does the proposed method mitigate simulation misspecification?\",\"answer\":\"It formulates the issue as a domain adaptation problem and uses a gradient reversal layer (GRL) approach to align representations between simulated and real data distributions.\"},{\"question\":\"Which population genetic methods and datasets are analyzed?\",\"answer\":\"The study focuses on SIA and ReLERNN, and uses the domain-adaptive SIA (dadaSIA) model to estimate improved selection coefficients on selected loci in the 1000 Genomes CEU population.\"}]","Domain-adaptive neural networks improve supervised machine learning based on simulated population genetic data | 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problem does the paper address in supervised population genetic machine learning?","Question",{"text":75,"@type":76},"It addresses failure caused by simulation misspecification, where simulated training data differs from real-world population genetic data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method mitigate simulation misspecification?",{"text":80,"@type":76},"It formulates the issue as a domain adaptation problem and uses a gradient reversal layer (GRL) approach to align representations between simulated and real data distributions.",{"name":82,"@type":73,"acceptedAnswer":83},"Which population genetic methods and datasets are analyzed?",{"text":84,"@type":76},"The study focuses on SIA and ReLERNN, and uses the domain-adaptive SIA (dadaSIA) model to estimate improved selection coefficients on selected loci in the 1000 Genomes CEU 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