Study of Evolution By Automating Hardy Weinberg Equilibrium With Machine Learning Techniques In TensorFlow And Keras

Karthika Balan, M. Santora, M. Faied, V. Carmona-Galindo
{"title":"Study of Evolution By Automating Hardy Weinberg Equilibrium With Machine Learning Techniques In TensorFlow And Keras","authors":"Karthika Balan, M. Santora, M. Faied, V. Carmona-Galindo","doi":"10.1109/ACCTHPA49271.2020.9213202","DOIUrl":null,"url":null,"abstract":"Evolution transpires in organism populations and includes population diversity, heredity and varying survival. One way to study evolution is to study how the frequency of a population’s allele varies from generation to generation. An allele is a type of gene variant. In other words, rather than looking at two parental species, evolution should be investigated by looking at the population as a whole. The only way to do this is therefore to investigate how allele frequencies shift in populations and how these shifts will predict what will happen to a population in the future. Throughout biology, there are basic equations which are used to model this theory basically termed as Hardy-Weinberg principle. This paper describes a new method that integrates Ecology and Engineering by automating the Hardy-Weinberg model using ADAM Optimization technique. The neural network trained based on Keras and Tensorflow has been deployed using python for the study of evolution. The developed model is tested with standard allele frequency database “ALFRED” to prove the efficiency.","PeriodicalId":191794,"journal":{"name":"2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACCTHPA49271.2020.9213202","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

Evolution transpires in organism populations and includes population diversity, heredity and varying survival. One way to study evolution is to study how the frequency of a population’s allele varies from generation to generation. An allele is a type of gene variant. In other words, rather than looking at two parental species, evolution should be investigated by looking at the population as a whole. The only way to do this is therefore to investigate how allele frequencies shift in populations and how these shifts will predict what will happen to a population in the future. Throughout biology, there are basic equations which are used to model this theory basically termed as Hardy-Weinberg principle. This paper describes a new method that integrates Ecology and Engineering by automating the Hardy-Weinberg model using ADAM Optimization technique. The neural network trained based on Keras and Tensorflow has been deployed using python for the study of evolution. The developed model is tested with standard allele frequency database “ALFRED” to prove the efficiency.
用机器学习技术在TensorFlow和Keras中自动化Hardy Weinberg平衡的进化研究
进化发生在生物种群中,包括种群多样性、遗传和不同的存活率。研究进化的一种方法是研究一个种群的等位基因的频率如何在一代一代之间变化。等位基因是一种基因变异。换句话说,不应该只观察两个亲本物种,而应该把整个种群作为一个整体来研究进化。因此,要做到这一点,唯一的方法就是研究等位基因频率在种群中是如何变化的,以及这些变化将如何预测一个种群未来会发生什么。在整个生物学中,有一些基本的方程用来模拟这个理论,基本上被称为Hardy-Weinberg原理。本文介绍了一种利用ADAM优化技术自动化Hardy-Weinberg模型,将生态学与工程学相结合的新方法。基于Keras和Tensorflow训练的神经网络已经使用python部署用于进化研究。用标准的等位基因频率数据库ALFRED对模型进行了测试,验证了模型的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信