Coracle--识别与连续变量相关细菌的机器学习框架

IF 4.4 3区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Bioinformatics Pub Date : 2023-12-19 DOI:10.1093/bioinformatics/btad749
Sebastian Staab, Anny Cardénas, Raquel S Peixoto, Falk Schreiber, Christian R Voolstra
{"title":"Coracle--识别与连续变量相关细菌的机器学习框架","authors":"Sebastian Staab, Anny Cardénas, Raquel S Peixoto, Falk Schreiber, Christian R Voolstra","doi":"10.1093/bioinformatics/btad749","DOIUrl":null,"url":null,"abstract":"Summary We present Coracle, an Artificial Intelligence (AI) framework that can identify associations between bacterial communities and continuous variables. Coracle uses an ensemble approach of prominent feature selection methods and machine learning (ML) models to identify features, i.e., bacteria, associated with a continuous variable, e.g. host thermal tolerance. The results are aggregated into a score that incorporates the performances of the different ML models and the respective feature importance, while also considering the robustness of feature selection. Additionally, regression coefficients provide first insights into the direction of the association. We show the utility of Coracle by analyzing associations between bacterial composition data (i.e., 16S rRNA Amplicon Sequence Variants, ASVs) and coral thermal tolerance (i.e., standardized short-term heat stress-derived diagnostics). This analysis identified high-scoring bacterial taxa that were previously found associated with coral thermal tolerance. Coracle scales with feature number and performs well with hundreds to thousands of features, corresponding to the typical size of current datasets. Coracle performs best if run at a higher taxonomic level first (e.g., order or family) to identify groups of interest that can subsequently be run at the ASV level. Availability and Implementation Coracle can be accessed via a dedicated web server that allows free and simple access: http://www.micportal.org/coracle/index. The underlying code is open-source and available via GitHub https://github.com/SebastianStaab/coracle.git. Supplementary information Example datasets and a tutorial are available on the web server webpage. Supplementary data are available at Bioinformatics online.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.4000,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Coracle—A Machine Learning Framework to Identify Bacteria Associated with Continuous Variables\",\"authors\":\"Sebastian Staab, Anny Cardénas, Raquel S Peixoto, Falk Schreiber, Christian R Voolstra\",\"doi\":\"10.1093/bioinformatics/btad749\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Summary We present Coracle, an Artificial Intelligence (AI) framework that can identify associations between bacterial communities and continuous variables. Coracle uses an ensemble approach of prominent feature selection methods and machine learning (ML) models to identify features, i.e., bacteria, associated with a continuous variable, e.g. host thermal tolerance. The results are aggregated into a score that incorporates the performances of the different ML models and the respective feature importance, while also considering the robustness of feature selection. Additionally, regression coefficients provide first insights into the direction of the association. We show the utility of Coracle by analyzing associations between bacterial composition data (i.e., 16S rRNA Amplicon Sequence Variants, ASVs) and coral thermal tolerance (i.e., standardized short-term heat stress-derived diagnostics). This analysis identified high-scoring bacterial taxa that were previously found associated with coral thermal tolerance. Coracle scales with feature number and performs well with hundreds to thousands of features, corresponding to the typical size of current datasets. Coracle performs best if run at a higher taxonomic level first (e.g., order or family) to identify groups of interest that can subsequently be run at the ASV level. Availability and Implementation Coracle can be accessed via a dedicated web server that allows free and simple access: http://www.micportal.org/coracle/index. The underlying code is open-source and available via GitHub https://github.com/SebastianStaab/coracle.git. Supplementary information Example datasets and a tutorial are available on the web server webpage. Supplementary data are available at Bioinformatics online.\",\"PeriodicalId\":8903,\"journal\":{\"name\":\"Bioinformatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2023-12-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Bioinformatics\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1093/bioinformatics/btad749\",\"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/btad749","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

摘要 我们介绍了一种人工智能(AI)框架--Coracle,它可以识别细菌群落与连续变量之间的关联。Coracle 采用了一种突出特征选择方法和机器学习(ML)模型的集合方法来识别与连续变量(如宿主热耐受性)相关的特征(即细菌)。结果汇总成一个分数,该分数综合了不同 ML 模型的性能和各自特征的重要性,同时还考虑了特征选择的鲁棒性。此外,回归系数还提供了关联方向的初步见解。我们通过分析细菌组成数据(即 16S rRNA 扩增子序列变异)与珊瑚耐热性(即标准化短期热应力诊断)之间的关联,展示了 Coracle 的实用性。这项分析确定了以前发现的与珊瑚耐热性相关的高分细菌类群。Coracle 可根据特征数量进行缩放,在数百到数千个特征的情况下表现良好,这与当前数据集的典型规模相当。如果先在较高的分类级别(如目或科)上运行 Coracle,以确定随后可在 ASV 级别上运行的感兴趣群组,则效果最佳。可用性与实现 Coracle 可通过专用网络服务器访问,访问免费且简单:http://www.micportal.org/coracle/index。底层代码是开源的,可通过 GitHub https://github.com/SebastianStaab/coracle.git 获取。补充信息 网络服务器网页上有示例数据集和教程。补充数据可在 Bioinformatics online 上获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Coracle—A Machine Learning Framework to Identify Bacteria Associated with Continuous Variables
Summary We present Coracle, an Artificial Intelligence (AI) framework that can identify associations between bacterial communities and continuous variables. Coracle uses an ensemble approach of prominent feature selection methods and machine learning (ML) models to identify features, i.e., bacteria, associated with a continuous variable, e.g. host thermal tolerance. The results are aggregated into a score that incorporates the performances of the different ML models and the respective feature importance, while also considering the robustness of feature selection. Additionally, regression coefficients provide first insights into the direction of the association. We show the utility of Coracle by analyzing associations between bacterial composition data (i.e., 16S rRNA Amplicon Sequence Variants, ASVs) and coral thermal tolerance (i.e., standardized short-term heat stress-derived diagnostics). This analysis identified high-scoring bacterial taxa that were previously found associated with coral thermal tolerance. Coracle scales with feature number and performs well with hundreds to thousands of features, corresponding to the typical size of current datasets. Coracle performs best if run at a higher taxonomic level first (e.g., order or family) to identify groups of interest that can subsequently be run at the ASV level. Availability and Implementation Coracle can be accessed via a dedicated web server that allows free and simple access: http://www.micportal.org/coracle/index. The underlying code is open-source and available via GitHub https://github.com/SebastianStaab/coracle.git. Supplementary information Example datasets and a tutorial are available on the web server webpage. Supplementary data are available at Bioinformatics online.
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.
期刊最新文献
MEHunter: Transformer-based mobile element variant detection from long reads PQSDC: a parallel lossless compressor for quality scores data via sequences partition and Run-Length prediction mapping. MUSE-XAE: MUtational Signature Extraction with eXplainable AutoEncoder enhances tumour types classification. CopyVAE: a variational autoencoder-based approach for copy number variation inference using single-cell transcriptomics CORDAX web server: An online platform for the prediction and 3D visualization of aggregation motifs in protein sequences.
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1