DeepPhylo: Phylogeny‐Aware Microbial Embeddings Enhanced Predictive Accuracy in Human Microbiome Data Analysis
Abstract Microbial data analysis poses significant challenges due to its high dimensionality, sparsity, and compositionality. Recent advances have shown that integrating abundance and phylogenetic information is an effective strategy for uncovering robust patterns and enhancing the predictive perfor...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
|
| Series: | Advanced Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/advs.202404277 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846141384202911744 |
|---|---|
| author | Bin Wang Yulong Shen Jingyan Fang Xiaoquan Su Zhenjiang Zech Xu |
| author_facet | Bin Wang Yulong Shen Jingyan Fang Xiaoquan Su Zhenjiang Zech Xu |
| author_sort | Bin Wang |
| collection | DOAJ |
| description | Abstract Microbial data analysis poses significant challenges due to its high dimensionality, sparsity, and compositionality. Recent advances have shown that integrating abundance and phylogenetic information is an effective strategy for uncovering robust patterns and enhancing the predictive performance in microbiome studies. However, existing methods primarily focus on the hierarchical structure of phylogenetic trees, overlooking the evolutionary distances embedded within them. This study introduces DeepPhylo, a novel method that employs phylogeny‐aware amplicon embeddings to effectively integrate abundance and phylogenetic information. DeepPhylo improves both the unsupervised discriminatory power and supervised predictive accuracy of microbiome data analysis. Compared to the existing methods, DeepPhylo demonstrates superiority in informing biologically relevant insights across five real‐world microbiome use cases, including clustering of skin microbiomes, prediction of host chronological age and gender, diagnosis of inflammatory bowel disease (IBD) across 15 studies, and multilabel disease classification. |
| format | Article |
| id | doaj-art-d6132095d19d4991bb55d7dfb1986f96 |
| institution | Kabale University |
| issn | 2198-3844 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj-art-d6132095d19d4991bb55d7dfb1986f962024-12-04T12:14:55ZengWileyAdvanced Science2198-38442024-12-011145n/an/a10.1002/advs.202404277DeepPhylo: Phylogeny‐Aware Microbial Embeddings Enhanced Predictive Accuracy in Human Microbiome Data AnalysisBin Wang0Yulong Shen1Jingyan Fang2Xiaoquan Su3Zhenjiang Zech Xu4School of Mathematics and Computer Sciences Nanchang University Nanchang 330031 ChinaSchool of Information Engineering Nanchang University Nanchang 330031 ChinaSchool of Mathematics and Computer Sciences Nanchang University Nanchang 330031 ChinaCollege of Computer Science and Technology Qingdao University Qingdao 266071 ChinaSchool of Mathematics and Computer Sciences Nanchang University Nanchang 330031 ChinaAbstract Microbial data analysis poses significant challenges due to its high dimensionality, sparsity, and compositionality. Recent advances have shown that integrating abundance and phylogenetic information is an effective strategy for uncovering robust patterns and enhancing the predictive performance in microbiome studies. However, existing methods primarily focus on the hierarchical structure of phylogenetic trees, overlooking the evolutionary distances embedded within them. This study introduces DeepPhylo, a novel method that employs phylogeny‐aware amplicon embeddings to effectively integrate abundance and phylogenetic information. DeepPhylo improves both the unsupervised discriminatory power and supervised predictive accuracy of microbiome data analysis. Compared to the existing methods, DeepPhylo demonstrates superiority in informing biologically relevant insights across five real‐world microbiome use cases, including clustering of skin microbiomes, prediction of host chronological age and gender, diagnosis of inflammatory bowel disease (IBD) across 15 studies, and multilabel disease classification.https://doi.org/10.1002/advs.202404277beta‐diversitydeep learningmicrobiomephylogeny |
| spellingShingle | Bin Wang Yulong Shen Jingyan Fang Xiaoquan Su Zhenjiang Zech Xu DeepPhylo: Phylogeny‐Aware Microbial Embeddings Enhanced Predictive Accuracy in Human Microbiome Data Analysis Advanced Science beta‐diversity deep learning microbiome phylogeny |
| title | DeepPhylo: Phylogeny‐Aware Microbial Embeddings Enhanced Predictive Accuracy in Human Microbiome Data Analysis |
| title_full | DeepPhylo: Phylogeny‐Aware Microbial Embeddings Enhanced Predictive Accuracy in Human Microbiome Data Analysis |
| title_fullStr | DeepPhylo: Phylogeny‐Aware Microbial Embeddings Enhanced Predictive Accuracy in Human Microbiome Data Analysis |
| title_full_unstemmed | DeepPhylo: Phylogeny‐Aware Microbial Embeddings Enhanced Predictive Accuracy in Human Microbiome Data Analysis |
| title_short | DeepPhylo: Phylogeny‐Aware Microbial Embeddings Enhanced Predictive Accuracy in Human Microbiome Data Analysis |
| title_sort | deepphylo phylogeny aware microbial embeddings enhanced predictive accuracy in human microbiome data analysis |
| topic | beta‐diversity deep learning microbiome phylogeny |
| url | https://doi.org/10.1002/advs.202404277 |
| work_keys_str_mv | AT binwang deepphylophylogenyawaremicrobialembeddingsenhancedpredictiveaccuracyinhumanmicrobiomedataanalysis AT yulongshen deepphylophylogenyawaremicrobialembeddingsenhancedpredictiveaccuracyinhumanmicrobiomedataanalysis AT jingyanfang deepphylophylogenyawaremicrobialembeddingsenhancedpredictiveaccuracyinhumanmicrobiomedataanalysis AT xiaoquansu deepphylophylogenyawaremicrobialembeddingsenhancedpredictiveaccuracyinhumanmicrobiomedataanalysis AT zhenjiangzechxu deepphylophylogenyawaremicrobialembeddingsenhancedpredictiveaccuracyinhumanmicrobiomedataanalysis |