  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.
