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, researchers from St. John’s University 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.
Original and true signals of ten cancer and control samples of the Breast data between 1500th and 1600th genes, and four randomly selected genes across 875 samples. The true signals of the samples appear to be smoother and more proximal to each other besides demonstrating less variations. The true signals of the genes obviously capture the global and subtle data characteristics better than the original ones.