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Predicting breast cancer using an expression values weighted clinical classifier

Overview of attention for article published in BMC Bioinformatics, December 2014
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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 (88th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

twitter
16 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
8 Dimensions

Readers on

mendeley
74 Mendeley
citeulike
2 CiteULike
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Title
Predicting breast cancer using an expression values weighted clinical classifier
Published in
BMC Bioinformatics, December 2014
DOI 10.1186/s12859-014-0411-1
Pubmed ID
Authors

Minta Thomas, Kris De Brabanter, Johan AK Suykens, Bart De Moor

Abstract

BackgroundClinical data, such as patient history, laboratory analysis, ultrasound parameters-which are the basis of day-to-day clinical decision support-are often used to guide the clinical management of cancer in the presence of microarray data. Several data fusion techniques are available to integrate genomics or proteomics data, but only a few studies have created a single prediction model using both gene expression and clinical data. These studies often remain inconclusive regarding an obtained improvement in prediction performance. To improve clinical management, these data should be fully exploited. This requires efficient algorithms to integrate these data sets and design a final classifier.LS-SVM classifiers and generalized eigenvalue/singular value decompositions are successfully used in many bioinformatics applications for prediction tasks. While bringing up the benefits of these two techniques, we propose a machine learning approach, a weighted LS-SVM classifier to integrate two data sources: microarray and clinical parameters.ResultsWe compared and evaluated the proposed methods on five breast cancer case studies. Compared to LS-SVM classifier on individual data sets, generalized eigenvalue decomposition (GEVD) and kernel GEVD, the proposed weighted LS-SVM classifier offers good prediction performance, in terms of test area under ROC Curve (AUC), on all breast cancer case studies.ConclusionsThus a clinical classifier weighted with microarray data set results in significantly improved diagnosis, prognosis and prediction responses to therapy. The proposed model has been shown as a promising mathematical framework in both data fusion and non-linear classification problems.

Twitter Demographics

The data shown below were collected from the profiles of 16 tweeters 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 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 3%
Malaysia 1 1%
Korea, Republic of 1 1%
Netherlands 1 1%
Sweden 1 1%
United States 1 1%
Unknown 67 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 27%
Researcher 18 24%
Student > Master 10 14%
Student > Bachelor 7 9%
Student > Doctoral Student 2 3%
Other 9 12%
Unknown 8 11%
Readers by discipline Count As %
Computer Science 17 23%
Medicine and Dentistry 15 20%
Agricultural and Biological Sciences 9 12%
Biochemistry, Genetics and Molecular Biology 6 8%
Psychology 4 5%
Other 12 16%
Unknown 11 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 07 March 2015.
All research outputs
#1,784,611
of 15,792,220 outputs
Outputs from BMC Bioinformatics
#665
of 5,710 outputs
Outputs of similar age
#36,362
of 305,820 outputs
Outputs of similar age from BMC Bioinformatics
#48
of 326 outputs
Altmetric has tracked 15,792,220 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,710 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has done well, scoring higher than 88% 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 305,820 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 88% of its contemporaries.
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 has done well, scoring higher than 85% of its contemporaries.