Stable Gene Selection from Microarray Data via Sample Weighting

Feature selection from gene expression microarray data is a widely used technique for selecting candidate genes in various cancer studies. Besides predictive ability of the selected genes, an important aspect in evaluating a selection method is the stability of the selected genes. Experts instinctiv...

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Veröffentlicht in:IEEE/ACM transactions on computational biology and bioinformatics Jg. 9; H. 1; S. 262 - 272
Hauptverfasser: Lei Yu, Yue Han, Berens, M. E.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.01.2012
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Bioinformatics
Cancer
classification
Computational Biology - methods
Data Mining - methods
Feature selection
Gene expression
gene expression microarray
Gene Expression Profiling
gene selection
Genes
Humans
Models, Genetic
Monte Carlo methods
Neoplasms - genetics
Neoplasms - metabolism
Oligonucleotide Array Sequence Analysis - methods
stability
Stability analysis
Studies
Support Vector Machine
Support vector machines
Training
ISSN:1545-5963, 1557-9964, 1557-9964
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Zusammenfassung:Feature selection from gene expression microarray data is a widely used technique for selecting candidate genes in various cancer studies. Besides predictive ability of the selected genes, an important aspect in evaluating a selection method is the stability of the selected genes. Experts instinctively have high confidence in the result of a selection method that selects similar sets of genes under some variations to the samples. However, a common problem of existing feature selection methods for gene expression data is that the selected genes by the same method often vary significantly with sample variations. In this work, we propose a general framework of sample weighting to improve the stability of feature selection methods under sample variations. The framework first weights each sample in a given training set according to its influence to the estimation of feature relevance, and then provides the weighted training set to a feature selection method. We also develop an efficient margin-based sample weighting algorithm under this framework. Experiments on a set of microarray data sets show that the proposed algorithm significantly improves the stability of representative feature selection algorithms such as SVM-RFE and ReliefF, without sacrificing their classification performance. Moreover, the proposed algorithm also leads to more stable gene signatures than the state-of-the-art ensemble method, particularly for small signature sizes.
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ISSN:1545-5963
1557-9964
1557-9964
DOI:10.1109/TCBB.2011.47