Improving Cancer Gene Expression Data Quality through a TCGA Data-Driven Evaluation of Identifier Filtering

Data reduction and data visualization for automatic diagnosis using gene expression and clinical data

Accurate diagnoses of specific diseases require, in general, the review of the whole medical history of a patient. Currently, even though many advances have been made for disease monitoring, domain experts are still requested to perform direct analyses in order to get a precise classification, thus implying significant efforts and costs. In this work we present a framework for automated diagnosis based on high-dimensional gene expression and clinical data. Given that high-dimensional data can be difficult to analyze and computationally expensive to process, we first perform data reduction to transform high-dimensional representations of data into a lower dimensional space, yet keeping them meaningful for our purposes. We used then different data visualization techniques to embed complex pieces of information in 2-D images, that are in turn used to perform diagnosis relying on deep learning approaches. Experimental analyses show that the proposed method achieves good performance, featuring a prediction Recall value between 91% and 99%.