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Decoding the complex genetic causes of heart diseases using systems biology

Overview of attention for article published in Biophysical Reviews, December 2014
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Title
Decoding the complex genetic causes of heart diseases using systems biology
Published in
Biophysical Reviews, December 2014
DOI 10.1007/s12551-014-0145-3
Pubmed ID
Authors

Djordje Djordjevic, Vinita Deshpande, Tomasz Szczesnik, Andrian Yang, David T. Humphreys, Eleni Giannoulatou, Joshua W. K. Ho

Abstract

The pace of disease gene discovery is still much slower than expected, even with the use of cost-effective DNA sequencing and genotyping technologies. It is increasingly clear that many inherited heart diseases have a more complex polygenic aetiology than previously thought. Understanding the role of gene-gene interactions, epigenetics, and non-coding regulatory regions is becoming increasingly critical in predicting the functional consequences of genetic mutations identified by genome-wide association studies and whole-genome or exome sequencing. A systems biology approach is now being widely employed to systematically discover genes that are involved in heart diseases in humans or relevant animal models through bioinformatics. The overarching premise is that the integration of high-quality causal gene regulatory networks (GRNs), genomics, epigenomics, transcriptomics and other genome-wide data will greatly accelerate the discovery of the complex genetic causes of congenital and complex heart diseases. This review summarises state-of-the-art genomic and bioinformatics techniques that are used in accelerating the pace of disease gene discovery in heart diseases. Accompanying this review, we provide an interactive web-resource for systems biology analysis of mammalian heart development and diseases, CardiacCode ( http://CardiacCode.victorchang.edu.au/ ). CardiacCode features a dataset of over 700 pieces of manually curated genetic or molecular perturbation data, which enables the inference of a cardiac-specific GRN of 280 regulatory relationships between 33 regulator genes and 129 target genes. We believe this growing resource will fill an urgent unmet need to fully realise the true potential of predictive and personalised genomic medicine in tackling human heart disease.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 17%
Unknown 5 83%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 2 33%
Professor > Associate Professor 2 33%
Student > Master 1 17%
Student > Ph. D. Student 1 17%
Readers by discipline Count As %
Engineering 2 33%
Unspecified 1 17%
Biochemistry, Genetics and Molecular Biology 1 17%
Computer Science 1 17%
Agricultural and Biological Sciences 1 17%
Other 0 0%

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 31 January 2015.
All research outputs
#3,931,240
of 4,693,373 outputs
Outputs from Biophysical Reviews
#27
of 56 outputs
Outputs of similar age
#131,724
of 163,906 outputs
Outputs of similar age from Biophysical Reviews
#8
of 17 outputs
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