Detecting pathway biomarkers of diabetic progression with differential entropy

Journal of Biomedical Informatics
Zhi-Ping Liu, Rui Gao

Abstract

Gene expression profiling techniques measure the transcriptional dynamics of thousands of genes in parallel manners. The available high-throughput transcriptomic datasets provide unprecedented opportunities of detecting biomarkers or signatures of complex diseases such as diabetes. In this work, we propose a computational method based on differential entropy to identify diabetic pathway biomarkers in rats from gene expression profiling data. We first collect the knowledgebase-documented pathways and map them with the corresponding gene expressions in control and disease samples, respectively. The pathway entropies are defined to evaluate their dysfunction-related activities and implications during the development and progression of type 2 diabetes. We rank these pathways via their differential status of entropy dynamics in the time series. The pathway biomarkers are then screened out by their classification ability of distinguishing diabetes from controls. The comparative studies with the other alternative methods demonstrate the effectiveness and advantage of our proposed strategy of biomarker identification. The classification performances on independent datasets further validate the diagnosis applicability of these identifie...Continue Reading

Citations

Dec 4, 2020·Current Diabetes Reports·Sanjay BasuSeth A Berkowitz
Nov 6, 2021·Metabolomics : Official Journal of the Metabolomic Society·Huajie ZhangLiming Shen
Dec 25, 2021·Analytical and Bioanalytical Chemistry·Chao LiXiaohui Lin

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