Integrative medical informatics approach for analyzing metabolic syndrome

Abdul Hafeez K; hro

doi:10.4172/2167-0943.C1.003

Integrative medical informatics approach for analyzing metabolic syndrome

International Conference on Metabolic Syndromes

October 17-18, 2016 Rome, Italy

Abdul Hafeez Kandhro

Mahidol University, Thailand

Posters & Accepted Abstracts: J Metabolic Synd

Abstract :

Dyslipidemia is one of the major forms of lipid disorder, which is characterized by increased triglyceride (TG), increased low-density lipoprotein-cholesterol (LDL-C) and decrease high-density lipoprotein-cholesterol (HDL-C) levels in blood. Recently, microRNAs (miRNAs) have been reported to involve in various biological processes, their potential usage as a biomarker as well as therapeutic marker in various diseases. Although searching disease related miRNAs is multifaceted due to expensive and time-consuming technologies with variable sensitivity and specificity. We used text-mining co-occurrence based approach for analyzing huge PubMed data to explore microRNA-lipid disease association. After retrieving and extracting information, construction of network was done by Cytoscape using edge-weighted tool to visualized significant associations. For miRNAs target predictions; existing network further extended with regulatory interaction network (RIN) by using CyTargetLinker Plug-in tool on Cytoscape. For biological process, associations of targeted genes were confirmed by gene ontology by using Biological Networks Gene Ontology BiNGO (GO) Plug-in tool on Cytoscape. We were text-mined 227 miRNA-disease associations including 148 miRNAs and four lipid diseases and five identifiers. The top 20 miRNA-disease was associated by Fisherâ�?�?s exact p-value 0.000034 to 0.033 and by TP score 0.0164 to 0.048. Significant GO terms were found on targeted genes for lipid, cholesterol, apolipoprotein and fatty acids. Present study could help future experimental studies, could walk around the biological functions and primary molecular mechanism of miRNAs in the development, progression, diagnosis and prognosis of lipid, cholesterol, and fatty acid disorders. However, additional computational tools and databases could provide broad perspective on relationships between miRNAs and disease.