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Gene selection in time-series gene expression data
- Adhikari, P. R. (author)
- Upadhyaya, B. B. (author)
-
- Meng, Chen (author)
- KTH,Beräkningsbiologi, CB
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- Hollmén, J. (author)
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- (creator_code:org_t)
- Berlin, Heidelberg : Springer Berlin Heidelberg, 2011
- 2011
- English.
- In: 6th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2011. - Berlin, Heidelberg : Springer Berlin Heidelberg. - 9783642248542 ; , s. 145-156
- Conference paper (peer-reviewed)
Abstract
Subject headings
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- The dimensionality of biological data is often very high. Feature selection can be used to tackle the problem of high dimensionality. However, majority of the work in feature selection consists of supervised feature selection methods which require class labels. The problem further escalates when the data is time-series gene expression measurements that measure the effect of external stimuli on biological system. In this paper we propose an unsupervised method for gene selection from time-series gene expression data founded on statistical significance testing and swap randomization. We perform experiments with a publicly available mouse gene expression dataset and also a human gene expression dataset describing the exposure to asbestos. The results in both datasets show a considerable decrease in number of genes.
Subject headings
- NATURVETENSKAP -- Biologi -- Bioinformatik och systembiologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences -- Bioinformatics and Systems Biology (hsv//eng)
Keyword
- Feature Selection
- Randomization
- Statistical Significance
- Time-series
- Biological data
- Class labels
- Data sets
- Expression measurements
- External stimulus
- Feature selection methods
- Gene selection
- High dimensionality
- Time-series gene expression data
- Unsupervised method
- Bioinformatics
- Feature extraction
- Mammals
- Gene expression
Publication and Content Type
- ref (subject category)
- kon (subject category)
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