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Systems analysis of gene ontology and biological pathways involved in post-myocardial infarction responses

Overview of attention for article published in BMC Genomics, June 2015
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Title
Systems analysis of gene ontology and biological pathways involved in post-myocardial infarction responses
Published in
BMC Genomics, June 2015
DOI 10.1186/1471-2164-16-s7-s18
Pubmed ID
Authors

Nguyen T Nguyen, Merry L Lindsey, Yu-Fang Jin

Abstract

Pathway analysis has been widely used to gain insight into essential mechanisms of the response to myocardial infarction (MI). Currently, there exist multiple pathway databases that organize molecular datasets and manually curate pathway maps for biological interpretation at varying forms of organization. However, inconsistencies among different databases in pathway descriptions, frequently due to conflicting results in the literature, can generate incorrect interpretations. Furthermore, although pathway analysis software provides detailed images of interactions among molecules, it does not exhibit how pathways interact with one another or with other biological processes under specific conditions. We propose a novel method to standardize descriptions of enriched pathways for a set of genes/proteins using Gene Ontology terms. We used this method to examine the relationships among pathways and biological processes for a set of condition-specific genes/proteins, represented as a functional biological pathway-process network. We applied this algorithm to a set of 613 MI-specific proteins we previously identified. A total of 96 pathways from Biocarta, KEGG, and Reactome, and 448 Gene Ontology Biological Processes were enriched with these 613 proteins. The pathways were represented as Boolean functions of biological processes, delivering an interactive scheme to organize enriched information with an emphasis on involvement of biological processes in pathways. We extracted a network focusing on MI to demonstrate that tyrosine phosphorylation of Signal Transducer and Activator of Transcription (STAT) protein, positive regulation of collagen metabolic process, coagulation, and positive/negative regulation of blood coagulation have immediate impacts on the MI response. Our method organized biological processes and pathways in an unbiased approach to provide an intuitive way to identify biological properties of pathways under specific conditions. Pathways from different databases have similar descriptions yet diverse biological processes, indicating variation in their ability to share similar functional characteristics. The coverages of pathways can be expanded with the incorporation of more biological processes, predicting involvement of protein members in pathways. Further, detailed analyses of the functional biological pathway-process network will allow researchers and scientists to explore critical routes in biological systems in the progression of disease.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 18%
Student > Ph. D. Student 4 18%
Student > Postgraduate 3 14%
Student > Bachelor 2 9%
Student > Master 1 5%
Other 0 0%
Unknown 8 36%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 23%
Biochemistry, Genetics and Molecular Biology 2 9%
Nursing and Health Professions 1 5%
Pharmacology, Toxicology and Pharmaceutical Science 1 5%
Immunology and Microbiology 1 5%
Other 3 14%
Unknown 9 41%
Attention Score in Context

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 24 June 2015.
All research outputs
#15,338,777
of 22,815,414 outputs
Outputs from BMC Genomics
#6,693
of 10,653 outputs
Outputs of similar age
#156,666
of 266,807 outputs
Outputs of similar age from BMC Genomics
#166
of 233 outputs
Altmetric has tracked 22,815,414 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,653 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 29th percentile – i.e., 29% of its peers scored the same or lower than it.
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We're also able to compare this research output to 233 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.