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arXiv · 1712.00336

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

Junfang Chen·Emanuel Schwarz
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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.

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Junfang Chen, Emanuel Schwarz. 2017-12-01. BioMM: Biologically-informed Multi-stage Machine learning for identification of epigenetic fingerprints. https://arxiv.org/abs/1712.00336

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