Transcriptome marker diagnostics using big data. Issue 1 (1st February 2016) - Han, Henry; Liu, Ying
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.