BioMM: Biologically-informed Multi-stage Machine learning for identification of epigenetic fingerprints

TL;DR

BioMM is a multi-stage machine learning framework that leverages biological pathway information to improve the identification of epigenetic patterns in high-dimensional data related to complex illnesses.

Contribution

This paper introduces BioMM, a novel biologically informed multi-stage machine learning approach that enhances pattern detection in high-dimensional biological data.

Findings

BioMM outperforms conventional machine learning methods.

It effectively utilizes biological pathway information.

Demonstrated on genome-wide DNA methylation data.

Abstract

The identification of reproducible biological patterns from high-dimensional data is a bottleneck for understanding the biology of complex illnesses such as schizophrenia. To address this, we developed a biologically informed, multi-stage machine learning (BioMM) framework. BioMM incorporates biological pathway information to stratify and aggregate high-dimensional biological data. We demonstrate the utility of this method using genome-wide DNA methylation data and show that it substantially outperforms conventional machine learning approaches. Therefore, the BioMM framework may be a fruitful machine learning strategy in high-dimensional data and be the basis for future, integrative analysis approaches.