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Dynamic association rules for gene expression data analysis

Overview of attention for article published in BMC Genomics, October 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 (81st percentile)

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1 blog
twitter
2 tweeters

Citations

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18 Dimensions

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56 Mendeley
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Title
Dynamic association rules for gene expression data analysis
Published in
BMC Genomics, October 2015
DOI 10.1186/s12864-015-1970-x
Pubmed ID
Authors

Shu-Chuan Chen, Tsung-Hsien Tsai, Cheng-Han Chung, Wen-Hsiung Li

Abstract

The purpose of gene expression analysis is to look for the association between regulation of gene expression levels and phenotypic variations. This association based on gene expression profile has been used to determine whether the induction/repression of genes correspond to phenotypic variations including cell regulations, clinical diagnoses and drug development. Statistical analyses on microarray data have been developed to resolve gene selection issue. However, these methods do not inform us of causality between genes and phenotypes. In this paper, we propose the dynamic association rule algorithm (DAR algorithm) which helps ones to efficiently select a subset of significant genes for subsequent analysis. The DAR algorithm is based on association rules from market basket analysis in marketing. We first propose a statistical way, based on constructing a one-sided confidence interval and hypothesis testing, to determine if an association rule is meaningful. Based on the proposed statistical method, we then developed the DAR algorithm for gene expression data analysis. The method was applied to analyze four microarray datasets and one Next Generation Sequencing (NGS) dataset: the Mice Apo A1 dataset, the whole genome expression dataset of mouse embryonic stem cells, expression profiling of the bone marrow of Leukemia patients, Microarray Quality Control (MAQC) data set and the RNA-seq dataset of a mouse genomic imprinting study. A comparison of the proposed method with the t-test on the expression profiling of the bone marrow of Leukemia patients was conducted. We developed a statistical way, based on the concept of confidence interval, to determine the minimum support and minimum confidence for mining association relationships among items. With the minimum support and minimum confidence, one can find significant rules in one single step. The DAR algorithm was then developed for gene expression data analysis. Four gene expression datasets showed that the proposed DAR algorithm not only was able to identify a set of differentially expressed genes that largely agreed with that of other methods, but also provided an efficient and accurate way to find influential genes of a disease. In the paper, the well-established association rule mining technique from marketing has been successfully modified to determine the minimum support and minimum confidence based on the concept of confidence interval and hypothesis testing. It can be applied to gene expression data to mine significant association rules between gene regulation and phenotype. The proposed DAR algorithm provides an efficient way to find influential genes that underlie the phenotypic variance.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Spain 1 2%
United States 1 2%
Taiwan 1 2%
Unknown 52 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 23%
Researcher 8 14%
Student > Master 8 14%
Student > Bachelor 3 5%
Professor > Associate Professor 3 5%
Other 8 14%
Unknown 13 23%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 27%
Biochemistry, Genetics and Molecular Biology 13 23%
Computer Science 7 13%
Sports and Recreations 2 4%
Economics, Econometrics and Finance 1 2%
Other 3 5%
Unknown 15 27%

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 19 October 2015.
All research outputs
#2,801,458
of 16,638,522 outputs
Outputs from BMC Genomics
#1,299
of 9,107 outputs
Outputs of similar age
#47,772
of 256,022 outputs
Outputs of similar age from BMC Genomics
#1
of 1 outputs
Altmetric has tracked 16,638,522 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 9,107 research outputs from this source. They receive a mean Attention Score of 4.3. 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 256,022 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 81% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them