Publication:
Towards a metagenomics machine learning interpretable model for understanding the transition from adenoma to colorectal cancer.

dc.contributor.authorCasimiro-Soriguer, Carlos S
dc.contributor.authorLoucera, Carlos
dc.contributor.authorPeña-Chilet, María
dc.contributor.authorDopazo, Joaquin
dc.date.accessioned2023-05-03T13:26:37Z
dc.date.available2023-05-03T13:26:37Z
dc.date.issued2022-01-10
dc.description.abstractGut microbiome is gaining interest because of its links with several diseases, including colorectal cancer (CRC), as well as the possibility of being used to obtain non-intrusive predictive disease biomarkers. Here we performed a meta-analysis of 1042 fecal metagenomic samples from seven publicly available studies. We used an interpretable machine learning approach based on functional profiles, instead of the conventional taxonomic profiles, to produce a highly accurate predictor of CRC with better precision than those of previous proposals. Moreover, this approach is also able to discriminate samples with adenoma, which makes this approach very promising for CRC prevention by detecting early stages in which intervention is easier and more effective. In addition, interpretable machine learning methods allow extracting features relevant for the classification, which reveals basic molecular mechanisms accounting for the changes undergone by the microbiome functional landscape in the transition from healthy gut to adenoma and CRC conditions. Functional profiles have demonstrated superior accuracy in predicting CRC and adenoma conditions than taxonomic profiles and additionally, in a context of explainable machine learning, provide useful hints on the molecular mechanisms operating in the microbiota behind these conditions.
dc.identifier.doi10.1038/s41598-021-04182-y
dc.identifier.essn2045-2322
dc.identifier.pmcPMC8748837
dc.identifier.pmid35013454
dc.identifier.pubmedURLhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8748837/pdf
dc.identifier.unpaywallURLhttps://www.nature.com/articles/s41598-021-04182-y.pdf
dc.identifier.urihttp://hdl.handle.net/10668/19580
dc.issue.number1
dc.journal.titleScientific reports
dc.journal.titleabbreviationSci Rep
dc.language.isoen
dc.organizationHospital Universitario Virgen del Rocío
dc.organizationInstituto de Biomedicina de Sevilla-IBIS
dc.page.number450
dc.pubmedtypeJournal Article
dc.pubmedtypeResearch Support, Non-U.S. Gov't
dc.pubmedtypeValidation Study
dc.rightsAttribution 4.0 International
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject.meshAdenoma
dc.subject.meshColorectal Neoplasms
dc.subject.meshGastrointestinal Microbiome
dc.subject.meshHumans
dc.subject.meshMachine Learning
dc.subject.meshMetagenomics
dc.titleTowards a metagenomics machine learning interpretable model for understanding the transition from adenoma to colorectal cancer.
dc.typeresearch article
dc.type.hasVersionVoR
dc.volume.number12
dspace.entity.typePublication

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
PMC8748837.pdf
Size:
2.03 MB
Format:
Adobe Portable Document Format