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Knowledge transfer via classification rules using functional mapping for integrative modeling of gene expression data

Overview of attention for article published in BMC Bioinformatics, July 2015
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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 (83rd percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

blogs
1 blog
twitter
5 tweeters

Citations

dimensions_citation
4 Dimensions

Readers on

mendeley
26 Mendeley
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Title
Knowledge transfer via classification rules using functional mapping for integrative modeling of gene expression data
Published in
BMC Bioinformatics, July 2015
DOI 10.1186/s12859-015-0643-8
Pubmed ID
Authors

Henry A. Ogoe, Shyam Visweswaran, Xinghua Lu, Vanathi Gopalakrishnan

Abstract

Most 'transcriptomic' data from microarrays are generated from small sample sizes compared to the large number of measured biomarkers, making it very difficult to build accurate and generalizable disease state classification models. Integrating information from different, but related, 'transcriptomic' data may help build better classification models. However, most proposed methods for integrative analysis of 'transcriptomic' data cannot incorporate domain knowledge, which can improve model performance. To this end, we have developed a methodology that leverages transfer rule learning and functional modules, which we call TRL-FM, to capture and abstract domain knowledge in the form of classification rules to facilitate integrative modeling of multiple gene expression data. TRL-FM is an extension of the transfer rule learner (TRL) that we developed previously. The goal of this study was to test our hypothesis that "an integrative model obtained via the TRL-FM approach outperforms traditional models based on single gene expression data sources". To evaluate the feasibility of the TRL-FM framework, we compared the area under the ROC curve (AUC) of models developed with TRL-FM and other traditional methods, using 21 microarray datasets generated from three studies on brain cancer, prostate cancer, and lung disease, respectively. The results show that TRL-FM statistically significantly outperforms TRL as well as traditional models based on single source data. In addition, TRL-FM performed better than other integrative models driven by meta-analysis and cross-platform data merging. The capability of utilizing transferred abstract knowledge derived from source data using feature mapping enables the TRL-FM framework to mimic the human process of learning and adaptation when performing related tasks. The novel TRL-FM methodology for integrative modeling for multiple 'transcriptomic' datasets is able to intelligently incorporate domain knowledge that traditional methods might disregard, to boost predictive power and generalization performance. In this study, TRL-FM's abstraction of knowledge is achieved in the form of functional modules, but the overall framework is generalizable in that different approaches of acquiring abstract knowledge can be integrated into this framework.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 4%
Unknown 25 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 23%
Student > Ph. D. Student 5 19%
Student > Master 3 12%
Student > Doctoral Student 2 8%
Professor > Associate Professor 2 8%
Other 6 23%
Unknown 2 8%
Readers by discipline Count As %
Computer Science 8 31%
Biochemistry, Genetics and Molecular Biology 5 19%
Medicine and Dentistry 2 8%
Business, Management and Accounting 2 8%
Agricultural and Biological Sciences 2 8%
Other 4 15%
Unknown 3 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 30 July 2015.
All research outputs
#1,297,170
of 10,444,782 outputs
Outputs from BMC Bioinformatics
#617
of 4,169 outputs
Outputs of similar age
#38,320
of 235,328 outputs
Outputs of similar age from BMC Bioinformatics
#20
of 114 outputs
Altmetric has tracked 10,444,782 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,169 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 85% 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 235,328 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 83% of its contemporaries.
We're also able to compare this research output to 114 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.