Transcriptome marker diagnostics using big data. Issue 1 (1st February 2016)

• Record Type: Journal Article
• Authors: Han, Henry; Liu, Ying
• Is Part Of: IET systems biology. Volume 10:Issue 1(2016)
• Page Start: 41
• Page End: 48
• Publication Date: 2016-02-01
• Subjects: bioinformatics -- patient diagnosis -- diseases -- RNA -- molecular biophysics -- Big Data -- medical diagnostic computing; transcriptome marker diagnostics -- translational bioinformatics -- rivalling‐clinical reproducible disease diagnosis -- machine learning -- big RNA‐seq data -- gene‐marker‐based diagnostic method -- network‐marker‐based diagnostic method -- big omics data diagnostics; Systems biology -- Periodicals; Cell physiology -- Periodicals; Biological systems -- Mathematical models -- Periodicals; Genetics -- Mathematical models -- Periodicals; Computational biology -- Periodicals; 573
• DOI: 10.1049/iet-syb.2015.0026
• Languages: English
• ISSNs: 1751-8849

Abstract

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.