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IPAD: the Integrated Pathway Analysis Database for Systematic Enrichment Analysis

Overview of attention for article published in BMC Bioinformatics, September 2012
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Title
IPAD: the Integrated Pathway Analysis Database for Systematic Enrichment Analysis
Published in
BMC Bioinformatics, September 2012
DOI 10.1186/1471-2105-13-s15-s7
Pubmed ID
Authors

Fan Zhang, Renee Drabier

Abstract

Next-Generation Sequencing (NGS) technologies and Genome-Wide Association Studies (GWAS) generate millions of reads and hundreds of datasets, and there is an urgent need for a better way to accurately interpret and distill such large amounts of data. Extensive pathway and network analysis allow for the discovery of highly significant pathways from a set of disease vs. healthy samples in the NGS and GWAS. Knowledge of activation of these processes will lead to elucidation of the complex biological pathways affected by drug treatment, to patient stratification studies of new and existing drug treatments, and to understanding the underlying anti-cancer drug effects. There are approximately 141 biological human pathway resources as of Jan 2012 according to the Pathguide database. However, most currently available resources do not contain disease, drug or organ specificity information such as disease-pathway, drug-pathway, and organ-pathway associations. Systematically integrating pathway, disease, drug and organ specificity together becomes increasingly crucial for understanding the interrelationships between signaling, metabolic and regulatory pathway, drug action, disease susceptibility, and organ specificity from high-throughput omics data (genomics, transcriptomics, proteomics and metabolomics).

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 113 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 2%
United States 2 2%
United Kingdom 2 2%
Germany 1 <1%
Switzerland 1 <1%
Portugal 1 <1%
Spain 1 <1%
Russia 1 <1%
Unknown 102 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 35 31%
Student > Ph. D. Student 21 19%
Student > Master 9 8%
Student > Bachelor 7 6%
Professor > Associate Professor 7 6%
Other 21 19%
Unknown 13 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 44 39%
Biochemistry, Genetics and Molecular Biology 18 16%
Medicine and Dentistry 14 12%
Computer Science 7 6%
Psychology 5 4%
Other 8 7%
Unknown 17 15%
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 01 November 2012.
All research outputs
#16,835,501
of 24,754,593 outputs
Outputs from BMC Bioinformatics
#5,570
of 7,581 outputs
Outputs of similar age
#113,495
of 175,094 outputs
Outputs of similar age from BMC Bioinformatics
#59
of 93 outputs
Altmetric has tracked 24,754,593 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,581 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
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We're also able to compare this research output to 93 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.