NDLI: Taxonomy-aware feature engineering for microbiome classification

Content Provider	Springer Nature : BioMed Central
Author	Oudah, Mai Henschel, Andreas
Abstract	Background What is a healthy microbiome? The pursuit of this and many related questions, especially in light of the recently recognized microbial component in a wide range of diseases has sparked a surge in metagenomic studies. They are often not simply attributable to a single pathogen but rather are the result of complex ecological processes. Relatedly, the increasing DNA sequencing depth and number of samples in metagenomic case-control studies enabled the applicability of powerful statistical methods, e.g. Machine Learning approaches. For the latter, the feature space is typically shaped by the relative abundances of operational taxonomic units, as determined by cost-effective phylogenetic marker gene profiles. While a substantial body of microbiome/microbiota research involves unsupervised and supervised Machine Learning, very little attention has been put on feature selection and engineering. Results We here propose the first algorithm to exploit phylogenetic hierarchy (i.e. an all-encompassing taxonomy) in feature engineering for microbiota classification. The rationale is to exploit the often mono- or oligophyletic distribution of relevant (but hidden) traits by virtue of taxonomic abstraction. The algorithm is embedded in a comprehensive microbiota classification pipeline, which we applied to a diverse range of datasets, distinguishing healthy from diseased microbiota samples. Conclusion We demonstrate substantial improvements over the state-of-the-art microbiota classification tools in terms of classification accuracy, regardless of the actual Machine Learning technique while using drastically reduced feature spaces. Moreover, generalized features bear great explanatory value: they provide a concise description of conditions and thus help to provide pathophysiological insights. Indeed, the automatically and reproducibly derived features are consistent with previously published domain expert analyses.
Related Links	https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-018-2205-3.pdf
Ending Page	13
Page Count	13
Starting Page	1
File Format	HTM / HTML
ISSN	14712105
DOI	10.1186/s12859-018-2205-3
Journal	BMC Bioinformatics
Issue Number	1
Volume Number	19
Language	English
Publisher	BioMed Central
Publisher Date	2018-06-15
Access Restriction	Open
Subject Keyword	Bioinformatics Microarrays Computational Biology Computer Appl. in Life Sciences Algorithms Feature engineering Supervised machine learning Microbiome Classification Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biochemistry Computer Science Applications Applied Mathematics Structural Biology
Journal Impact Factor	2.9/2023
5-Year Journal Impact Factor	3.6/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Deep self-supervised machine learning algorithms with a novel feature elimination and selection approaches for blood test-based multi-dimensional health risks classification

FeatureSelect: a software for feature selection based on machine learning approaches

Methodology for biomarker discovery with reproducibility in microbiome data using machine learning

Semi-supervised Learning for the BioNLP Gene Regulation Network

Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification

Towards a supervised classification of neocortical interneuron morphologies

Robust identification of molecular phenotypes using semi-supervised learning

Ensemble of rankers for efficient gene signature extraction in smoke exposure classification

An application of machine learning with feature selection to improve diagnosis and classification of neurodegenerative disorders

Taxonomy-aware feature engineering for microbiome classification

Similar Documents

Deep self-supervised machine learning algorithms with a novel feature elimination and selection approaches for blood test-based multi-dimensional health risks classification

FeatureSelect: a software for feature selection based on machine learning approaches

Methodology for biomarker discovery with reproducibility in microbiome data using machine learning

Semi-supervised Learning for the BioNLP Gene Regulation Network

Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification

Towards a supervised classification of neocortical interneuron morphologies

Robust identification of molecular phenotypes using semi-supervised learning

Ensemble of rankers for efficient gene signature extraction in smoke exposure classification

An application of machine learning with feature selection to improve diagnosis and classification of neurodegenerative disorders

Taxonomy-aware feature engineering for microbiome classification