MicroHDF: predicting host phenotypes with metagenomic data using a deep forest-based framework.

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Kai Shi, Qiaohui Liu, Qingrong Ji, Qisheng He, Xing-Ming Zhao
{"title":"MicroHDF: predicting host phenotypes with metagenomic data using a deep forest-based framework.","authors":"Kai Shi, Qiaohui Liu, Qingrong Ji, Qisheng He, Xing-Ming Zhao","doi":"10.1093/bib/bbae530","DOIUrl":null,"url":null,"abstract":"<p><p>The gut microbiota plays a vital role in human health, and significant effort has been made to predict human phenotypes, especially diseases, with the microbiota as a promising indicator or predictor with machine learning (ML) methods. However, the accuracy is impacted by a lot of factors when predicting host phenotypes with the metagenomic data, e.g. small sample size, class imbalance, high-dimensional features, etc. To address these challenges, we propose MicroHDF, an interpretable deep learning framework to predict host phenotypes, where a cascade layers of deep forest units is designed for handling sample class imbalance and high dimensional features. The experimental results show that the performance of MicroHDF is competitive with that of existing state-of-the-art methods on 13 publicly available datasets of six different diseases. In particular, it performs best with the area under the receiver operating characteristic curve of 0.9182 ± 0.0098 and 0.9469 ± 0.0076 for inflammatory bowel disease (IBD) and liver cirrhosis, respectively. Our MicroHDF also shows better performance and robustness in cross-study validation. Furthermore, MicroHDF is applied to two high-risk diseases, IBD and autism spectrum disorder, as case studies to identify potential biomarkers. In conclusion, our method provides an effective and reliable prediction of the host phenotype and discovers informative features with biological insights.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"25 6","pages":""},"PeriodicalIF":6.8000,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11500453/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Briefings in bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/bib/bbae530","RegionNum":2,"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

Abstract

The gut microbiota plays a vital role in human health, and significant effort has been made to predict human phenotypes, especially diseases, with the microbiota as a promising indicator or predictor with machine learning (ML) methods. However, the accuracy is impacted by a lot of factors when predicting host phenotypes with the metagenomic data, e.g. small sample size, class imbalance, high-dimensional features, etc. To address these challenges, we propose MicroHDF, an interpretable deep learning framework to predict host phenotypes, where a cascade layers of deep forest units is designed for handling sample class imbalance and high dimensional features. The experimental results show that the performance of MicroHDF is competitive with that of existing state-of-the-art methods on 13 publicly available datasets of six different diseases. In particular, it performs best with the area under the receiver operating characteristic curve of 0.9182 ± 0.0098 and 0.9469 ± 0.0076 for inflammatory bowel disease (IBD) and liver cirrhosis, respectively. Our MicroHDF also shows better performance and robustness in cross-study validation. Furthermore, MicroHDF is applied to two high-risk diseases, IBD and autism spectrum disorder, as case studies to identify potential biomarkers. In conclusion, our method provides an effective and reliable prediction of the host phenotype and discovers informative features with biological insights.

MicroHDF:利用基于深度森林的框架,通过元基因组数据预测宿主表型。
肠道微生物群对人类健康起着至关重要的作用,人们已经做出了巨大努力,利用微生物群作为机器学习(ML)方法的一个有前途的指标或预测因子来预测人类表型,特别是疾病。然而,在利用元基因组数据预测宿主表型时,准确性受到很多因素的影响,如样本量小、类不平衡、高维特征等。为了应对这些挑战,我们提出了一种可解释的深度学习框架--MicroHDF,用于预测宿主表型,其中设计了一个级联层的深度森林单元,用于处理样本类不平衡和高维特征。实验结果表明,在六种不同疾病的 13 个公开数据集上,MicroHDF 的性能与现有的最先进方法相比具有竞争力。特别是,它在炎症性肠病(IBD)和肝硬化的接收者工作特征曲线下面积分别为 0.9182 ± 0.0098 和 0.9469 ± 0.0076,表现最佳。我们的 MicroHDF 在交叉研究验证中也表现出更好的性能和稳健性。此外,我们还将 MicroHDF 应用于两种高风险疾病(IBD 和自闭症谱系障碍)的案例研究,以确定潜在的生物标记物。总之,我们的方法能有效、可靠地预测宿主表型,并发现具有生物学洞察力的信息特征。
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
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
×
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