02053nas a2200181   4500008004100000022001400041245013200055210006900187260001600256300000800272490000700280520135300287100003301640700002001673700002401693700002001717856013401737       2022                        eng d  a2045-232200aTowards a metagenomics machine learning interpretable model for understanding the transition from adenoma to colorectal cancer.0 aTowards a metagenomics machine learning interpretable model for   c2022 Jan 10  a4500 v123 a<p>Gut 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.</p>1 aCasimiro-Soriguer, Carlos, S1 aLoucera, Carlos1 aPeña-Chilet, Maria1 aDopazo, Joaquin  uhttps://www.clinbioinfosspa.es/content/towards-metagenomics-machine-learning-interpretable-model-understanding-transition-adenoma