Integrated Theory- and Data-driven Feature Selection in Gene Expression Data Analysis

The exponential growth of high dimensional biological data has led to a rapid increase in demand for automated approaches for knowledge production. Existing methods rely on two general approaches to address this challenge: 1) the Theory-driven approach, which utilizes prior accumulated knowledge, and 2) the Data-driven approach, which solely utilizes the data to deduce scientific knowledge. Both of these approaches alone suffer from bias toward past/present knowledge, as they fail to incorporate all of the current knowledge that is available to make new discoveries. In this paper, we show how an integrated method can effectively address the high dimensionality of big biological data, which is a major problem for pure data-driven analysis approaches. We realize our approach in a novel two-step analytical workflow that incorporates a new feature selection paradigm as the first step to handling high-throughput gene expression data analysis and that utilizes graphical causal modeling as the second step to handle the automatic extraction of causal relationships. Our results, on real-world clinical datasets from The Cancer Genome Atlas (TCGA), demonstrate that our method is capable of intelligently selecting genes for learning effective causal networks.