Network Transformation of Gene Expression for Feature Extraction

Classical approaches to analyze transcriptomic data usually produce average classification models that have very low reproducibility. In this work, genome wide gene expression is considered through the activity of large regulatory networks. We introduce a new measure of regulatory influence based on the variations of expression of genes in a large inferred regulatory network. This methodology can be used to transform transcriptomic data into a smaller influence data set on which feature selection and classification models show similar predictive performance and increased stability and reproducibility, especially when comparing models trained on different datasets. The methodology was tested on two distinct bladder cancer data sets.