A machine learning pipeline for discriminant pathways identification

TL;DR

This paper introduces a versatile machine learning pipeline that combines molecular profiling with network comparison to identify key gene pathways affected by diseases, demonstrated through applications to air pollution susceptibility and neurodegenerative disorders.

Contribution

It presents a novel, flexible pipeline integrating machine learning and network analysis for detecting disease-related pathway disruptions in biological data.

Findings

Successfully identified disease-affected pathways in three case studies

Pipeline is independent of the classification algorithm used

Provides a new tool for systems biology and network medicine research

Abstract

Motivation: Identifying the molecular pathways more prone to disruption during a pathological process is a key task in network medicine and, more in general, in systems biology. Results: In this work we propose a pipeline that couples a machine learning solution for molecular profiling with a recent network comparison method. The pipeline can identify changes occurring between specific sub-modules of networks built in a case-control biomarker study, discriminating key groups of genes whose interactions are modified by an underlying condition. The proposal is independent from the classification algorithm used. Three applications on genomewide data are presented regarding children susceptibility to air pollution and two neurodegenerative diseases: Parkinson's and Alzheimer's. Availability: Details about the software used for the experiments discussed in this paper are provided in the Appendix.