Combining Machine Learning and Network Analysis Pipelines: The Case of Microbiome and Metabolomics Data in Colorectal Cancer

Eleni Ntzioni; Ioanna Chouvarda

doi:10.3233/SHTI210965

Combining Machine Learning and Network Analysis Pipelines: The Case of Microbiome and Metabolomics Data in Colorectal Cancer

Stud Health Technol Inform. 2022 Jan 14:289:489-490. doi: 10.3233/SHTI210965.

Authors

Eleni Ntzioni¹, Ioanna Chouvarda¹

Affiliation

¹ Laboratory of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.

PMID: 35062198
DOI: 10.3233/SHTI210965

Abstract

This study analyzes samples of intestinal microbiome and metabolites, from healthy individuals (HE) and patients with adenomas (AD) or colorectal carcinomas (CRC). A network analysis (NetAn) method was applied to the data, to identify the metabolites and microbial genera associated with the 3 classes and then 7 classification models were used. The models were initially trained with classic feature selection vs features resulting from NetAn. The distinction of HE and AD is successful, while CRC distinction presented lower success.

Keywords: Machine learning; metabolomics; microbiome; network analysis.

MeSH terms

Colorectal Neoplasms*
Gastrointestinal Microbiome*
Humans
Machine Learning
Metabolomics
Microbiota*