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

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