Paper Info
Title
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 similar to 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 similar to 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.
Year
Venue
Keywords
2016
2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)
microbiome, ibd, kegg, random forest, pca, machine learning
Field
DocType
Citations 
Training set,Data mining,Protein family,Disease,Autoimmune condition,Computer science,Microbiome,KEGG,Genomics,Data sequences,Artificial intelligence,Machine learning
Conference
1
PageRank 
References 
Authors
0.41
7
6
Name
Order
Citations
PageRank
mehrdad yazdani163.39
Bryn Taylor210.41
Justine Debelius341.46
Weizhong Li4363.60
Rob Knight536626.19
L. Smarr692.45