ArticleBioinformation2015
Application of centrality measures in the identification of critical genes in diabetes mellitus.
Article in Bioinformation, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed, 9 citations in OpenAlex.
- Objective Assessment of Cerebellar Ataxia: A Comprehensive and Refined Approach.Scientific reports · 2020Article
- Compile- and run-time approaches for the selection of efficient data structures for dynamic graph analysis.Applied network science · 2016Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The connectivity of a protein and its structure is related to its functional properties. Many experimental approaches have been employed for the identification of Diabetes Mellitus (DM) associated candidate genes. Therefore, it is of interest to use var ious graph centrality measures integrated with the genes associated with the human Diabetes Mellitus network for the identification of potential targets. We used 2728 genes known to cause Diabetes Mellitus from Jensenlab (Novo Nordisk Foundation Center for Protein Research, Denmark) for this analysis. A protein-protein interaction network was further constructed using a tool Centralities in Biological Networks (CentiBiN) with 1020 nodes after eliminating the duplicates, parallel edges, self -loop edges and unknown Human Protein Reference Database (HPRD) IDS. We used fourteen centralities measures which are useful in identifying the structural characteristic of individuals in the network. The results of the centrality measures are highly correlated. Thus, we identified genes that are critically associated with DM. We further report the top ten genes of all fourteen centrality measures for further consideration as targets for DM.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.