Gene selection in time-series gene expression data

• 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.

Ämnesord

NATURVETENSKAP  -- Biologi -- Bioinformatik och systembiologi (hsv//swe)

NATURAL SCIENCES  -- Biological Sciences -- Bioinformatics and Systems Biology (hsv//eng)

Nyckelord

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

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