Using machine learning to identify major shifts in human gut microbiome protein family abundance in disease

Abstract

Inflammatory Bowel Disease (IBD) is an autoimmune condition that is observed to be associated with major alterations in the gut microbiome taxonomic composition. Here we classify major changes in microbiome protein family abundances between healthy subjects and IBD patients. We use machine learning to analyze results obtained previously from computing relative abundance of ~10,000 KEGG orthologous protein families in the gut microbiome of a set of healthy individuals and IBD patients. We develop a machine learning pipeline, involving the Kolomogorv-Smirnov test, to identify the 100 most statistically significant entries in the KEGG database. Then we use these 100 as a training set for a Random Forest classifier to determine ~5% the KEGGs which are best at separating disease and healthy states. Lastly, we developed a Natural Language Processing classifier of the KEGG description files to predict KEGG relative over-or under-abundance. As we expand our analysis from 10,000 KEGG protein families to one million proteins identified in the gut microbiome, scalable methods for quickly identifying such anomalies between health and disease states will be increasingly valuable for biological interpretation of sequence data.

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