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Conference Paper: A Changing Window Approach to Exploring Gene Expression Patterns

TitleA Changing Window Approach to Exploring Gene Expression Patterns
Authors
Keywordsfeature weighting
gene expression analysis
microarray analysis
subspace clustering
Issue Date2008
PublisherIEEE.
Citation
IEEE International Conference on Bioinformatics and Biomedicine (BIBM '08), Philadelphia, PA, 3-5 November 2008, p. 301 - 304 How to Cite?
AbstractThis paper presents a changing window approach to exploring gene expression patterns in 'snapshot windows'. A snapshot window is a sub-matrix of co-expressed microarray data representing certain expression pattern. In this approach, we use a feature weighting k-means subspace clustering algorithm to generate a set of clusters and each cluster defines a set of 'snapshot windows' which are characterized by different sets of ordered sample weights that were assigned by the clustering algorithm. We define an accumulated weighting threshold (AWT) as the sum of weights of samples in the 'snapshot window'. Given a cluster, different 'snapshot windows' can be obtained by changing AWT to explore all possible local expression patterns in the cluster. Experiment results have shown our approach is effective and flexible in exploring various expression patterns and identifying novel ones.
Persistent Identifierhttp://hdl.handle.net/10722/223759
ISBN

 

DC FieldValueLanguage
dc.contributor.authorWang, Q-
dc.contributor.authorYe, YM-
dc.contributor.authorHuang, JZ-
dc.date.accessioned2016-03-14T07:54:04Z-
dc.date.available2016-03-14T07:54:04Z-
dc.date.issued2008-
dc.identifier.citationIEEE International Conference on Bioinformatics and Biomedicine (BIBM '08), Philadelphia, PA, 3-5 November 2008, p. 301 - 304-
dc.identifier.isbn978-0-7695-3452-7-
dc.identifier.urihttp://hdl.handle.net/10722/223759-
dc.description.abstractThis paper presents a changing window approach to exploring gene expression patterns in 'snapshot windows'. A snapshot window is a sub-matrix of co-expressed microarray data representing certain expression pattern. In this approach, we use a feature weighting k-means subspace clustering algorithm to generate a set of clusters and each cluster defines a set of 'snapshot windows' which are characterized by different sets of ordered sample weights that were assigned by the clustering algorithm. We define an accumulated weighting threshold (AWT) as the sum of weights of samples in the 'snapshot window'. Given a cluster, different 'snapshot windows' can be obtained by changing AWT to explore all possible local expression patterns in the cluster. Experiment results have shown our approach is effective and flexible in exploring various expression patterns and identifying novel ones.-
dc.languageeng-
dc.publisherIEEE.-
dc.relation.ispartofIEEE International Conference on Bioinformatics and Biomedicine (BIBM)-
dc.rightsIEEE International Conference on Bioinformatics and Biomedicine (BIBM). Copyright © IEEE.-
dc.rights©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectfeature weighting-
dc.subjectgene expression analysis-
dc.subjectmicroarray analysis-
dc.subjectsubspace clustering-
dc.titleA Changing Window Approach to Exploring Gene Expression Patterns-
dc.typeConference_Paper-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/BIBM.2008.39-
dc.identifier.hkuros164904-
dc.identifier.spage301-
dc.identifier.epage304-
dc.publisher.placePhiladelphia, PA-

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