Identification of Chronic Hypersensitivity Pneumonitis Biomarkers with Machine Learning and Differential Co-expression Analysis

Aims: This study aims to identify the biomarkers for chronic hypersensitivity pneumoni -tis (CHP) and facilitate the precise gene therapy of CHP. Background: Chronic hypersensitivity pneumonitis (CHP) is an interstitial lung disease caused by hypersensitive reactions to inhaled antigens. Clinically, the task of differentiating CHP and other in-terstitial lung diseases, especially idiopathic pulmonary fibrosis (IPF), was challenging. Objective: In this study, we analyzed the publically available gene expression profile of 82 CHP pa-tients, 103 IPF patients, and 103 control samples to identify the CHP biomarkers. Methods: The CHP biomarkers were selected with advanced feature selection methods: Monte Car-lo Feature Selection (MCFS) and Incremental Feature Selection (IFS). A Support Vector Machine (SVM) classifier was built. Then, we analyzed these CHP biomarkers through functional enrich-ment analysis and differential co-expression analysis. Results: There were 674 identified CHP biomarkers. The co-expression network of these biomark-ers in CHP included more negative regulations and the network structure of CHP was quite differ-ent from the network of IPF and control. Conclusion: The SVM classifier may serve as an important clinical tool to address the challenging task of differentiating between CHP and IPF. Many of the biomarker genes on the differential co-expression network showed great promise in revealing the underlying mechanisms of CHP.