Transcriptome marker diagnostics using big data.

Abstract

The big omics data are challenging translational bioinformatics in an unprecedented way for its complexities and volumes. How to employ big omics data to achieve a rivalling-clinical, reproducible disease diagnosis from a systems approach is an urgent problem to be solved in translational bioinformatics and machine learning. In this study, the authors propose a novel transcriptome marker diagnosis to tackle this problem using big RNA-seq data by viewing whole transcriptome as a profile marker systematically. The systems diagnosis not only avoids the reproducibility issue of the existing gene-/network-marker-based diagnostic methods, but also achieves rivalling-clinical diagnostic results by extracting true signals from big RNA-seq data. Their method demonstrates a better fit for personalised diagnostics by attaining exceptional diagnostic performance via using systems information than its competitive methods and prepares itself as a good candidate for clinical usage. To the best of their knowledge, it is the first study on this topic and will inspire the more investigations in big omics data diagnostics.