  The potential benefits of applying machine learning methods to -omics data
are becoming increasingly apparent, especially in clinical settings. However,
the unique characteristics of these data are not always well suited to machine
learning techniques. These data are often generated across different
technologies in different labs, and frequently with high dimensionality. In
this paper we present a framework for combining -omics data sets, and for
handling high dimensional data, making -omics research more accessible to
machine learning applications. We demonstrate the success of this framework
through integration and analysis of multi-analyte data for a set of 3,533
breast cancers. We then use this data-set to predict breast cancer patient
survival for individuals at risk of an impending event, with higher accuracy
and lower variance than methods trained on individual data-sets. We hope that
our pipelines for data-set generation and transformation will open up -omics
data to machine learning researchers. We have made these freely available for
noncommercial use at www.ccg.ai.
