Title
A PRObabilistic Pathway Score (PROPS) for Classification with Applications to Inflammatory Bowel Disease.
Abstract
Gene-based supervised machine learning classification models have been widely used to differentiate disease states, predict disease progression and determine effective treatment options. However, many of these classifiers are sensitive to noise and frequently do not replicate in external validation sets. For complex, heterogeneous diseases, these classifiers are further limited by being unable to capture varying combinations of genes that lead to the same phenotype. Pathway-based classification can overcome these challenges by using robust, aggregate features to represent biological mechanisms. In this work, we developed a novel pathway-based approach, PRObabilistic Pathway Score, which uses genes to calculate individualized pathway scores for classification. Unlike previous individualized pathway-based classification methods that use gene sets, we incorporate gene interactions using probabilistic graphical models to more accurately represent the underlying biology and achieve better performance. We apply our method to differentiate two similar complex diseases, ulcerative colitis (UC) and Crohn's disease (CD), which are the two main types of inflammatory bowel disease (IBD). Using five IBD datasets, we compare our method against four gene-based and four alternative pathway-based classifiers in distinguishing CD from UC. We demonstrate superior classification performance and provide biological insight into the top pathways separating CD from UC.
Year
DOI
Venue
2018
10.1093/bioinformatics/btx651
BIOINFORMATICS
Field
DocType
Volume
Protein Interaction Map,Computer science,Systems biology,Disease progression,Bioinformatics,Probabilistic logic,Gene regulatory network,Inflammatory bowel disease
Journal
34
Issue
ISSN
Citations 
6
1367-4803
1
PageRank 
References 
Authors
0.41
8
8