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Lung Cancer Signature Biomarkers: tissue specific semantic similarity based clustering of Digital Differential Display (DDD) data

Overview of attention for article published in BMC Research Notes, November 2012
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Title
Lung Cancer Signature Biomarkers: tissue specific semantic similarity based clustering of Digital Differential Display (DDD) data
Published in
BMC Research Notes, November 2012
DOI 10.1186/1756-0500-5-617
Pubmed ID
Authors

Mousami Srivastava, Pankaj Khurana, Ragumani Sugadev

Abstract

The tissue-specific Unigene Sets derived from more than one million expressed sequence tags (ESTs) in the NCBI, GenBank database offers a platform for identifying significantly and differentially expressed tissue-specific genes by in-silico methods. Digital differential display (DDD) rapidly creates transcription profiles based on EST comparisons and numerically calculates, as a fraction of the pool of ESTs, the relative sequence abundance of known and novel genes. However, the process of identifying the most likely tissue for a specific disease in which to search for candidate genes from the pool of differentially expressed genes remains difficult. Therefore, we have used 'Gene Ontology semantic similarity score' to measure the GO similarity between gene products of lung tissue-specific candidate genes from control (normal) and disease (cancer) sets. This semantic similarity score matrix based on hierarchical clustering represents in the form of a dendrogram. The dendrogram cluster stability was assessed by multiple bootstrapping. Multiple bootstrapping also computes a p-value for each cluster and corrects the bias of the bootstrap probability.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 4%
Unknown 23 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 29%
Researcher 4 17%
Student > Master 3 13%
Other 1 4%
Student > Bachelor 1 4%
Other 3 13%
Unknown 5 21%
Readers by discipline Count As %
Medicine and Dentistry 6 25%
Agricultural and Biological Sciences 6 25%
Biochemistry, Genetics and Molecular Biology 3 13%
Computer Science 2 8%
Unspecified 1 4%
Other 1 4%
Unknown 5 21%
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 04 November 2012.
All research outputs
#18,319,742
of 22,684,168 outputs
Outputs from BMC Research Notes
#3,006
of 4,253 outputs
Outputs of similar age
#140,571
of 184,200 outputs
Outputs of similar age from BMC Research Notes
#58
of 75 outputs
Altmetric has tracked 22,684,168 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 4,253 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 16th percentile – i.e., 16% 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 184,200 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 75 others from the same source and published within six weeks on either side of this one. This one is in the 6th percentile – i.e., 6% of its contemporaries scored the same or lower than it.