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Advances in translational bioinformatics facilitate revealing the landscape of complex disease mechanisms

Overview of attention for article published in BMC Bioinformatics, December 2014
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1 tweeter

Citations

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3 Dimensions

Readers on

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18 Mendeley
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2 CiteULike
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Title
Advances in translational bioinformatics facilitate revealing the landscape of complex disease mechanisms
Published in
BMC Bioinformatics, December 2014
DOI 10.1186/1471-2105-15-s17-i1
Pubmed ID
Authors

Jack Y Yang, A Keith Dunker, Jun S Liu, Xiang Qin, Hamid R Arabnia, William Yang, Andrzej Niemierko, Zhongxue Chen, Zuojie Luo, Liangjiang Wang, Yunlong Liu, Dong Xu, Youping Deng, Weida Tong, Mary Qu Yang

Abstract

Advances of high-throughput technologies have rapidly produced more and more data from DNAs and RNAs to proteins, especially large volumes of genome-scale data. However, connection of the genomic information to cellular functions and biological behaviours relies on the development of effective approaches at higher systems level. In particular, advances in RNA-Seq technology has helped the studies of transcriptome, RNA expressed from the genome, while systems biology on the other hand provides more comprehensive pictures, from which genes and proteins actively interact to lead to cellular behaviours and physiological phenotypes. As biological interactions mediate many biological processes that are essential for cellular function or disease development, it is important to systematically identify genomic information including genetic mutations from GWAS (genome-wide association study), differentially expressed genes, bidirectional promoters, intrinsic disordered proteins (IDP) and protein interactions to gain deep insights into the underlying mechanisms of gene regulations and networks. Furthermore, bidirectional promoters can co-regulate many biological pathways, where the roles of bidirectional promoters can be studied systematically for identifying co-regulating genes at interactive network level. Combining information from different but related studies can ultimately help revealing the landscape of molecular mechanisms underlying complex diseases such as cancer.

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 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 18 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 22%
Professor 3 17%
Student > Bachelor 2 11%
Student > Ph. D. Student 2 11%
Student > Master 2 11%
Other 2 11%
Unknown 3 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 22%
Computer Science 4 22%
Agricultural and Biological Sciences 2 11%
Arts and Humanities 1 6%
Immunology and Microbiology 1 6%
Other 3 17%
Unknown 3 17%

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 19 January 2015.
All research outputs
#13,222,146
of 16,638,522 outputs
Outputs from BMC Bioinformatics
#5,057
of 5,984 outputs
Outputs of similar age
#212,670
of 309,356 outputs
Outputs of similar age from BMC Bioinformatics
#287
of 326 outputs
Altmetric has tracked 16,638,522 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,984 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one is in the 7th percentile – i.e., 7% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 309,356 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 326 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.