A fuzzy approach for analyzing outliers in gene expression data

Noha Yousri, Mohamed S. Kamel, Mohamed A. Ismail

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Outlier gene expression patterns identify abnormal gene behavior, possibly indicating the deviation in gene function for certain tumor types. It may also reveal novel gene-tumor relations, as well as novel tumor types. This is important in designing drugs for tumors as well as in studying the functional relations between genes. Apart from identifying outliers, associating outliers to the clusters in a gene expression dataset can reveal information about the source of outliers, i.e. to which tumors they are related. This work proposes a fuzzy approach which combines outlier detection and clustering results, to analyze gene expression outliers based on their relations to clusters. Both outlier detection and clustering are done based on a connectivity-based clustering algorithm, and the results are then combined using an iterative technique that propagates fuzzy memberships from patterns to their neighbors. Experimental results on leukaemia expression patterns are used to illustrate the proposed approach.

Original languageEnglish
Title of host publicationBioMedical Engineering and Informatics
Subtitle of host publicationNew Development and the Future - Proceedings of the 1st International Conference on BioMedical Engineering and Informatics, BMEI 2008
Pages207-213
Number of pages7
Volume1
DOIs
Publication statusPublished - 17 Sep 2008
Externally publishedYes
EventBioMedical Engineering and Informatics: New Development and the Future - 1st International Conference on BioMedical Engineering and Informatics, BMEI 2008 - Sanya, Hainan, China
Duration: 27 May 200830 May 2008

Other

OtherBioMedical Engineering and Informatics: New Development and the Future - 1st International Conference on BioMedical Engineering and Informatics, BMEI 2008
CountryChina
CitySanya, Hainan
Period27/5/0830/5/08

Fingerprint

Gene expression
Tumors
Genes
Clustering algorithms

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing
  • Biomedical Engineering

Cite this

Yousri, N., Kamel, M. S., & Ismail, M. A. (2008). A fuzzy approach for analyzing outliers in gene expression data. In BioMedical Engineering and Informatics: New Development and the Future - Proceedings of the 1st International Conference on BioMedical Engineering and Informatics, BMEI 2008 (Vol. 1, pp. 207-213). [4548663] https://doi.org/10.1109/BMEI.2008.274

A fuzzy approach for analyzing outliers in gene expression data. / Yousri, Noha; Kamel, Mohamed S.; Ismail, Mohamed A.

BioMedical Engineering and Informatics: New Development and the Future - Proceedings of the 1st International Conference on BioMedical Engineering and Informatics, BMEI 2008. Vol. 1 2008. p. 207-213 4548663.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Yousri, N, Kamel, MS & Ismail, MA 2008, A fuzzy approach for analyzing outliers in gene expression data. in BioMedical Engineering and Informatics: New Development and the Future - Proceedings of the 1st International Conference on BioMedical Engineering and Informatics, BMEI 2008. vol. 1, 4548663, pp. 207-213, BioMedical Engineering and Informatics: New Development and the Future - 1st International Conference on BioMedical Engineering and Informatics, BMEI 2008, Sanya, Hainan, China, 27/5/08. https://doi.org/10.1109/BMEI.2008.274
Yousri N, Kamel MS, Ismail MA. A fuzzy approach for analyzing outliers in gene expression data. In BioMedical Engineering and Informatics: New Development and the Future - Proceedings of the 1st International Conference on BioMedical Engineering and Informatics, BMEI 2008. Vol. 1. 2008. p. 207-213. 4548663 https://doi.org/10.1109/BMEI.2008.274
Yousri, Noha ; Kamel, Mohamed S. ; Ismail, Mohamed A. / A fuzzy approach for analyzing outliers in gene expression data. BioMedical Engineering and Informatics: New Development and the Future - Proceedings of the 1st International Conference on BioMedical Engineering and Informatics, BMEI 2008. Vol. 1 2008. pp. 207-213
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