The fuzzy gene filter: An adaptive fuzzy inference system for expression array feature selection

Meir Perez; David M. Rubin; Tshilidzi Marwala; Lesley E. Scott; Jonathan Featherston; Wendy Stevens

doi:10.1007/978-3-642-13033-5_7

The fuzzy gene filter: An adaptive fuzzy inference system for expression array feature selection

Meir Perez
, David M. Rubin
, Tshilidzi Marwala
, Lesley E. Scott
, Jonathan Featherston
, Wendy Stevens

Faculty of Engineering and the Built Environment

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Citations (Scopus)

Abstract

The identification of class differentiating genes is central to microarray data classification. Genes are ranked in order of differential expression and the optimal top ranking genes are selected as features for classification. In this paper, a new approach to gene ranking, based on a fuzzy inference system - the Fuzzy Gene Filter - is presented and compared to classical ranking approaches (the t-test, Wilcoxon test and ROC analysis). Two performance metrics are used; maximum Separability Index and highest cross-validation accuracy. The techniques were implemented on two publically available data-sets. The Fuzzy Gene Filter outperformed the other techniques both with regards to maximum Separability Index, as well as highest cross-validation accuracy. For the prostate data-set it a attained a Leave-one-out cross-validation accuracy of 96.1% and for the lymphoma data-set, 100%. The Fuzzy Gene Filter cross-validation accuracies were also higher than those recorded in previous publications which used the same data-sets. The Fuzzy Gene Filter's success is ascribed to its incorporation of both parametric and non-parametric data features and its ability to be optimised to suit the specific data-set under analysis.

Original language	English
Title of host publication	Trends in Applied Intelligent Systems - 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2010, Proceedings
Pages	62-71
Number of pages	10
Edition	PART 3
DOIs	https://doi.org/10.1007/978-3-642-13033-5_7
Publication status	Published - 2010
Event	23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligence Systems, IEA/AIE 2010 - Cordoba, Spain Duration: 1 Jun 2010 → 4 Jun 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 3
Volume	6098 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligence Systems, IEA/AIE 2010
Country/Territory	Spain
City	Cordoba
Period	1/06/10 → 4/06/10

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-13033-5_7

Cite this

Perez, M., Rubin, D. M., Marwala, T., Scott, L. E., Featherston, J., & Stevens, W. (2010). The fuzzy gene filter: An adaptive fuzzy inference system for expression array feature selection. In Trends in Applied Intelligent Systems - 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2010, Proceedings (PART 3 ed., pp. 62-71). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6098 LNAI, No. PART 3). https://doi.org/10.1007/978-3-642-13033-5_7

Perez, Meir ; Rubin, David M. ; Marwala, Tshilidzi et al. / The fuzzy gene filter : An adaptive fuzzy inference system for expression array feature selection. Trends in Applied Intelligent Systems - 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2010, Proceedings. PART 3. ed. 2010. pp. 62-71 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).

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abstract = "The identification of class differentiating genes is central to microarray data classification. Genes are ranked in order of differential expression and the optimal top ranking genes are selected as features for classification. In this paper, a new approach to gene ranking, based on a fuzzy inference system - the Fuzzy Gene Filter - is presented and compared to classical ranking approaches (the t-test, Wilcoxon test and ROC analysis). Two performance metrics are used; maximum Separability Index and highest cross-validation accuracy. The techniques were implemented on two publically available data-sets. The Fuzzy Gene Filter outperformed the other techniques both with regards to maximum Separability Index, as well as highest cross-validation accuracy. For the prostate data-set it a attained a Leave-one-out cross-validation accuracy of 96.1\% and for the lymphoma data-set, 100\%. The Fuzzy Gene Filter cross-validation accuracies were also higher than those recorded in previous publications which used the same data-sets. The Fuzzy Gene Filter's success is ascribed to its incorporation of both parametric and non-parametric data features and its ability to be optimised to suit the specific data-set under analysis.",

author = "Meir Perez and Rubin, \{David M.\} and Tshilidzi Marwala and Scott, \{Lesley E.\} and Jonathan Featherston and Wendy Stevens",

year = "2010",

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Perez, M, Rubin, DM, Marwala, T, Scott, LE, Featherston, J & Stevens, W 2010, The fuzzy gene filter: An adaptive fuzzy inference system for expression array feature selection. in Trends in Applied Intelligent Systems - 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2010, Proceedings. PART 3 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 6098 LNAI, pp. 62-71, 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligence Systems, IEA/AIE 2010, Cordoba, Spain, 1/06/10. https://doi.org/10.1007/978-3-642-13033-5_7

The fuzzy gene filter: An adaptive fuzzy inference system for expression array feature selection. / Perez, Meir; Rubin, David M.; Marwala, Tshilidzi et al.
Trends in Applied Intelligent Systems - 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2010, Proceedings. PART 3. ed. 2010. p. 62-71 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6098 LNAI, No. PART 3).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Scott, Lesley E.

AU - Featherston, Jonathan

AU - Stevens, Wendy

PY - 2010

Y1 - 2010

N2 - The identification of class differentiating genes is central to microarray data classification. Genes are ranked in order of differential expression and the optimal top ranking genes are selected as features for classification. In this paper, a new approach to gene ranking, based on a fuzzy inference system - the Fuzzy Gene Filter - is presented and compared to classical ranking approaches (the t-test, Wilcoxon test and ROC analysis). Two performance metrics are used; maximum Separability Index and highest cross-validation accuracy. The techniques were implemented on two publically available data-sets. The Fuzzy Gene Filter outperformed the other techniques both with regards to maximum Separability Index, as well as highest cross-validation accuracy. For the prostate data-set it a attained a Leave-one-out cross-validation accuracy of 96.1% and for the lymphoma data-set, 100%. The Fuzzy Gene Filter cross-validation accuracies were also higher than those recorded in previous publications which used the same data-sets. The Fuzzy Gene Filter's success is ascribed to its incorporation of both parametric and non-parametric data features and its ability to be optimised to suit the specific data-set under analysis.

AB - The identification of class differentiating genes is central to microarray data classification. Genes are ranked in order of differential expression and the optimal top ranking genes are selected as features for classification. In this paper, a new approach to gene ranking, based on a fuzzy inference system - the Fuzzy Gene Filter - is presented and compared to classical ranking approaches (the t-test, Wilcoxon test and ROC analysis). Two performance metrics are used; maximum Separability Index and highest cross-validation accuracy. The techniques were implemented on two publically available data-sets. The Fuzzy Gene Filter outperformed the other techniques both with regards to maximum Separability Index, as well as highest cross-validation accuracy. For the prostate data-set it a attained a Leave-one-out cross-validation accuracy of 96.1% and for the lymphoma data-set, 100%. The Fuzzy Gene Filter cross-validation accuracies were also higher than those recorded in previous publications which used the same data-sets. The Fuzzy Gene Filter's success is ascribed to its incorporation of both parametric and non-parametric data features and its ability to be optimised to suit the specific data-set under analysis.

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ER -

Perez M, Rubin DM, Marwala T, Scott LE, Featherston J, Stevens W. The fuzzy gene filter: An adaptive fuzzy inference system for expression array feature selection. In Trends in Applied Intelligent Systems - 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2010, Proceedings. PART 3 ed. 2010. p. 62-71. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). doi: 10.1007/978-3-642-13033-5_7

The fuzzy gene filter: An adaptive fuzzy inference system for expression array feature selection

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