利用生成对抗网络进行系统发育推断。

IF 4.4 3区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Megan L Smith, Matthew W Hahn
{"title":"利用生成对抗网络进行系统发育推断。","authors":"Megan L Smith, Matthew W Hahn","doi":"10.1093/bioinformatics/btad543","DOIUrl":null,"url":null,"abstract":"<p><strong>Motivation: </strong>The application of machine learning approaches in phylogenetics has been impeded by the vast model space associated with inference. Supervised machine learning approaches require data from across this space to train models. Because of this, previous approaches have typically been limited to inferring relationships among unrooted quartets of taxa, where there are only three possible topologies. Here, we explore the potential of generative adversarial networks (GANs) to address this limitation. GANs consist of a generator and a discriminator: at each step, the generator aims to create data that is similar to real data, while the discriminator attempts to distinguish generated and real data. By using an evolutionary model as the generator, we use GANs to make evolutionary inferences. Since a new model can be considered at each iteration, heuristic searches of complex model spaces are possible. Thus, GANs offer a potential solution to the challenges of applying machine learning in phylogenetics.</p><p><strong>Results: </strong>We developed phyloGAN, a GAN that infers phylogenetic relationships among species. phyloGAN takes as input a concatenated alignment, or a set of gene alignments, and infers a phylogenetic tree either considering or ignoring gene tree heterogeneity. We explored the performance of phyloGAN for up to 15 taxa in the concatenation case and 6 taxa when considering gene tree heterogeneity. Error rates are relatively low in these simple cases. However, run times are slow and performance metrics suggest issues during training. Future work should explore novel architectures that may result in more stable and efficient GANs for phylogenetics.</p><p><strong>Availability and implementation: </strong>phyloGAN is available on github: https://github.com/meganlsmith/phyloGAN/.</p>","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":"39 9","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500083/pdf/","citationCount":"0","resultStr":"{\"title\":\"Phylogenetic inference using generative adversarial networks.\",\"authors\":\"Megan L Smith, Matthew W Hahn\",\"doi\":\"10.1093/bioinformatics/btad543\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Motivation: </strong>The application of machine learning approaches in phylogenetics has been impeded by the vast model space associated with inference. Supervised machine learning approaches require data from across this space to train models. Because of this, previous approaches have typically been limited to inferring relationships among unrooted quartets of taxa, where there are only three possible topologies. Here, we explore the potential of generative adversarial networks (GANs) to address this limitation. GANs consist of a generator and a discriminator: at each step, the generator aims to create data that is similar to real data, while the discriminator attempts to distinguish generated and real data. By using an evolutionary model as the generator, we use GANs to make evolutionary inferences. Since a new model can be considered at each iteration, heuristic searches of complex model spaces are possible. Thus, GANs offer a potential solution to the challenges of applying machine learning in phylogenetics.</p><p><strong>Results: </strong>We developed phyloGAN, a GAN that infers phylogenetic relationships among species. phyloGAN takes as input a concatenated alignment, or a set of gene alignments, and infers a phylogenetic tree either considering or ignoring gene tree heterogeneity. We explored the performance of phyloGAN for up to 15 taxa in the concatenation case and 6 taxa when considering gene tree heterogeneity. Error rates are relatively low in these simple cases. However, run times are slow and performance metrics suggest issues during training. Future work should explore novel architectures that may result in more stable and efficient GANs for phylogenetics.</p><p><strong>Availability and implementation: </strong>phyloGAN is available on github: https://github.com/meganlsmith/phyloGAN/.</p>\",\"PeriodicalId\":8903,\"journal\":{\"name\":\"Bioinformatics\",\"volume\":\"39 9\",\"pages\":\"\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2023-09-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500083/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Bioinformatics\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1093/bioinformatics/btad543\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/bioinformatics/btad543","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0

摘要

动机机器学习方法在系统发育学中的应用一直受到与推理相关的巨大模型空间的阻碍。有监督的机器学习方法需要来自整个空间的数据来训练模型。正因为如此,以前的方法通常仅限于推断无根四分类群之间的关系,因为在这种情况下只有三种可能的拓扑结构。在此,我们将探索生成对抗网络(GANs)的潜力,以解决这一局限性。生成式对抗网络由生成器和判别器组成:在每一步中,生成器的目标是创建与真实数据相似的数据,而判别器则试图区分生成的数据和真实数据。通过使用进化模型作为生成器,我们利用 GANs 进行进化推断。由于每次迭代都可以考虑新的模型,因此可以对复杂的模型空间进行启发式搜索。因此,GANs 为在系统发育学中应用机器学习所面临的挑战提供了潜在的解决方案:我们开发了phyloGAN,这是一种能推断物种间系统发育关系的GAN。phyloGAN的输入是一条连接排列或一组基因排列,然后推断出一棵考虑或忽略基因树异质性的系统发育树。我们对phyloGAN的性能进行了探索,在并列情况下最多可推算15个分类群,而在考虑基因树异质性时最多可推算6个分类群。在这些简单的情况下,错误率相对较低。不过,运行时间较慢,而且性能指标表明在训练过程中存在问题。未来的工作应该探索新的架构,从而为系统发育学带来更稳定、更高效的 GAN。可用性和实现:phyloGAN 可在 github 上获取:https://github.com/meganlsmith/phyloGAN/。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Phylogenetic inference using generative adversarial networks.

Phylogenetic inference using generative adversarial networks.

Phylogenetic inference using generative adversarial networks.

Phylogenetic inference using generative adversarial networks.

Motivation: The application of machine learning approaches in phylogenetics has been impeded by the vast model space associated with inference. Supervised machine learning approaches require data from across this space to train models. Because of this, previous approaches have typically been limited to inferring relationships among unrooted quartets of taxa, where there are only three possible topologies. Here, we explore the potential of generative adversarial networks (GANs) to address this limitation. GANs consist of a generator and a discriminator: at each step, the generator aims to create data that is similar to real data, while the discriminator attempts to distinguish generated and real data. By using an evolutionary model as the generator, we use GANs to make evolutionary inferences. Since a new model can be considered at each iteration, heuristic searches of complex model spaces are possible. Thus, GANs offer a potential solution to the challenges of applying machine learning in phylogenetics.

Results: We developed phyloGAN, a GAN that infers phylogenetic relationships among species. phyloGAN takes as input a concatenated alignment, or a set of gene alignments, and infers a phylogenetic tree either considering or ignoring gene tree heterogeneity. We explored the performance of phyloGAN for up to 15 taxa in the concatenation case and 6 taxa when considering gene tree heterogeneity. Error rates are relatively low in these simple cases. However, run times are slow and performance metrics suggest issues during training. Future work should explore novel architectures that may result in more stable and efficient GANs for phylogenetics.

Availability and implementation: phyloGAN is available on github: https://github.com/meganlsmith/phyloGAN/.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Bioinformatics
Bioinformatics 生物-生化研究方法
CiteScore
11.20
自引率
5.20%
发文量
753
审稿时长
2.1 months
期刊介绍: The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.
×
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:481959085
Book学术官方微信