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Extracting proteins involved in disease progression using temporally connected networks

Overview of attention for article published in BMC Systems Biology, July 2018
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Title
Extracting proteins involved in disease progression using temporally connected networks
Published in
BMC Systems Biology, July 2018
DOI 10.1186/s12918-018-0600-z
Pubmed ID
Authors

Rajat Anand, Dipanka Tanu Sarmah, Samrat Chatterjee

Abstract

Metabolic disorders such as obesity and diabetes are diseases which develop gradually over time in an individual and through the perturbations of genes. Systematic experiments tracking disease progression at gene level are usually conducted giving a temporal microarray data. There is a need for developing methods to analyze such complex data and extract important proteins which could be involved in temporal progression of the data and hence progression of the disease. In the present study, we have considered a temporal microarray data from an experiment conducted to study development of obesity and diabetes in mice. We have used this data along with an available Protein-Protein Interaction network to find a network of interactions between proteins which reproduces the next time point data from previous time point data. We show that the resulting network can be mined to identify critical nodes involved in the temporal progression of perturbations. We further show that published algorithms can be applied on such connected network to mine important proteins and show an overlap between outputs from published and our algorithms. The importance of set of proteins identified was supported by literature as well as was further validated by comparing them with the positive genes dataset from OMIM database which shows significant overlap. The critical proteins identified from algorithms can be hypothesized to play important role in temporal progression of the data.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 3 30%
Researcher 3 30%
Other 1 10%
Student > Ph. D. Student 1 10%
Student > Master 1 10%
Other 1 10%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 40%
Unspecified 3 30%
Agricultural and Biological Sciences 2 20%
Medicine and Dentistry 1 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 October 2018.
All research outputs
#12,069,596
of 13,595,754 outputs
Outputs from BMC Systems Biology
#939
of 1,078 outputs
Outputs of similar age
#231,150
of 267,601 outputs
Outputs of similar age from BMC Systems Biology
#1
of 2 outputs
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