ArticleCurrent genomics2017
System Level Meta-analysis of Microarray Datasets for Elucidation of Diabetes Mellitus Pathobiology.
Article in Current genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning Model Based on Insulin Resistance Metagenes Underpins Genetic Basis of Type 2 Diabetes.Biomolecules · 2023Pooled it
- Diabetic vasculopathy: macro and microvascular injury.Current pathobiology reports · 2020Article
- Transcriptome profiling reveals association of peripheral adipose tissue pathology with type-2 diabetes in Asian Indians.Adipocyte · 2019Article
- A Network Biology Approach for Assessing the Role of Pathologic Adipose Tissues in Insulin Resistance Using Meta-analysis of Microarray Datasets.Current genomics · 2018Article
- Systems Genomics of Thigh Adipose Tissue From Asian Indian Type-2 Diabetics Revealed Distinct Protein Interaction Hubs.Frontiers in genetics · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundType 2 diabetes (T2D) is a common multi-factorial disease that is primarily ac-counted to ineffective insulin action in lowering blood glucose level and later escalates to impaired insu-lin secretion by pancreatic β cells. Deregulation in insulin signaling to its target organs is attributed to this disease phenotype. Various genome-wide microarray studies from multiple insulin responsive tis-sues have been conducted in past but due to inherent noise in microarray data and heterogeneity in dis-ease etiology; reproduction of prioritized pathways/genes is very low across various studies.
objectiveIn this study, we aim to identify consensus signaling and metabolic pathways through system level meta-analysis of multiple expression-sets to elucidate T2D pathobiology.
methodWe used 'R', an open source statistical environment, which is routinely used for Microarray data analysis particularly using special sets of packages available at Bioconductor. We primarily focused on gene-set analysis methods to elucidate various aspects of T2D.
resultLiterature-based evidences have shown the success of our approach in exploring various known aspects of diabetes pathophysiology.
conclusionOur study stressed the need to develop novel bioinformatics workflows to advance our understanding further in insulin signaling.
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Registered trials
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