↓ Skip to main content

Visualization of genetic disease-phenotype similarities by multiple maps t-SNE with Laplacian regularization

Overview of attention for article published in BMC Medical Genomics, January 2014
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

blogs
1 blog
twitter
1 tweeter

Citations

dimensions_citation
13 Dimensions

Readers on

mendeley
25 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Visualization of genetic disease-phenotype similarities by multiple maps t-SNE with Laplacian regularization
Published in
BMC Medical Genomics, January 2014
DOI 10.1186/1755-8794-7-s2-s1
Pubmed ID
Authors

Weiwei Xu, Xingpeng Jiang, Xiaohua Hu, Guangrong Li

Abstract

From a phenotypic standpoint, certain types of diseases may prove to be difficult to accurately diagnose, due to specific combinations of confounding symptoms. Referred to as phenotypic overlap, these sets of disease-related symptoms suggest shared pathophysiological mechanisms. Few attempts have been made to visualize the phenotypic relationships between different human diseases from a machine learning perspective. The proposed research, it is anticipated, will visually assist researchers in quickly disambiguating symptoms which can confound the timely and accurate diagnosis of a disease.

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

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 36%
Researcher 5 20%
Student > Master 2 8%
Student > Postgraduate 2 8%
Professor > Associate Professor 2 8%
Other 3 12%
Unknown 2 8%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 20%
Agricultural and Biological Sciences 4 16%
Computer Science 3 12%
Mathematics 2 8%
Medicine and Dentistry 2 8%
Other 6 24%
Unknown 3 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 17 August 2016.
All research outputs
#2,344,883
of 14,381,119 outputs
Outputs from BMC Medical Genomics
#127
of 758 outputs
Outputs of similar age
#39,851
of 234,058 outputs
Outputs of similar age from BMC Medical Genomics
#6
of 34 outputs
Altmetric has tracked 14,381,119 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 758 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 83% of its peers.
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 234,058 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.