dc.contributor.author Hu, Y
dc.contributor.author Pang, S
dc.contributor.author Havukkala, I
dc.date.accessioned 2011-08-08T05:43:50Z
dc.date.available 2011-08-08T05:43:50Z
dc.date.copyright 2006
dc.date.issued 2011-08-08
dc.identifier.citation The 6th International Conference on Hybrid Intelligent Systems (HIS’06), Rio de Janeiro, Brazil and 4th Conference on Neuro-Computing and Evolving Intelligence (NCEI’06), Auckland, New Zealand, pages 56 - 61
dc.identifier.isbn 0-7695-2662-4
dc.identifier.uri http://hdl.handle.net/10292/1620
dc.description.abstract Consistency modeling for gene selection is a new topic emerging from recent cancer bioinformatics research. The result of classification or clustering on a training set was often found very different from the same operations on a testing set. Here, we address this issue as a consistency problem. We propose a new concept of performance-based consistency and a new novel gene selection method, Genetic Algorithm Gene Selection method in terms of consistency (GAGSc). The proposed consistency concept and GAGSc method were investigated on eight benchmark microarray and proteomic datasets. The experimental results show that the different microarray datasets have different consistency characteristics, and that better consistency can lead to an unbiased and reproducible outcome with good disease prediction accuracy. More importantly, GAGSc has demonstrated that gene selection, with the proposed consistency measurement, is able to enhance the reproducibility in microarray diagnosis experiments.
dc.publisher IEEE Computer Society Press
dc.rights (c) 2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works.
dc.title A novel microarray gene selection method based on consistency
dc.type Conference Contribution
dc.rights.accessrights OpenAccess
dc.identifier.doi 10.1109/HIS.2006.264897

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