A. D. Soloshenkov, E. A. Soloshenkova, M. T. Semina, N. N. Spasskaya, V. N. Voronkova, Y. A. Stolpovky
{"title":"Artificial Intelligence and Classical Methods in Animal Genetics and Breeding","authors":"A. D. Soloshenkov, E. A. Soloshenkova, M. T. Semina, N. N. Spasskaya, V. N. Voronkova, Y. A. Stolpovky","doi":"10.1134/s1022795424700297","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">\n<b>Abstract</b>—</h3><p>Basic methods of population genetics and animal breeding and mathematical methods of machine learning used in animal breeding are analyzed. CatBoost library models were trained on the example of two domesticated species—horse (<i>Equus caballus</i>) and reindeer (<i>Rangifer tarandus</i>). Data from microsatellite panels of loci 16 and 17, respectively, were used to train the model using data on domesticated and wild reindeer, European and Russian horse breeds. The standard indicators (Accuracy, Precision, Recall, and <i>F1</i>) were calculated, and confusion matrices were constructed to assess the success of the model. New possibilities for identifying animal breed affiliation are shown.</p>","PeriodicalId":21441,"journal":{"name":"Russian Journal of Genetics","volume":"15 1","pages":""},"PeriodicalIF":0.6000,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Russian Journal of Genetics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1134/s1022795424700297","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0
Abstract—
Basic methods of population genetics and animal breeding and mathematical methods of machine learning used in animal breeding are analyzed. CatBoost library models were trained on the example of two domesticated species—horse (Equus caballus) and reindeer (Rangifer tarandus). Data from microsatellite panels of loci 16 and 17, respectively, were used to train the model using data on domesticated and wild reindeer, European and Russian horse breeds. The standard indicators (Accuracy, Precision, Recall, and F1) were calculated, and confusion matrices were constructed to assess the success of the model. New possibilities for identifying animal breed affiliation are shown.
期刊介绍：
Russian Journal of Genetics is a journal intended to make significant contribution to the development of genetics. The journal publishes reviews and experimental papers in the areas of theoretical and applied genetics. It presents fundamental research on genetic processes at molecular, cell, organism, and population levels, including problems of the conservation and rational management of genetic resources and the functional genomics, evolutionary genomics and medical genetics.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
