Gene trajectory clustering with a hybrid genetic algorithm and expectation maximization method
| dc.contributor.author | Chan, Z. | |
| dc.contributor.author | Kasabov, N | |
| dc.date.accessioned | 2009-05-27T22:18:54Z | |
| dc.date.available | 2009-05-27T22:18:54Z | |
| dc.date.copyright | 2004 | |
| dc.date.created | 2004 | |
| dc.date.issued | 2004 | |
| dc.description.abstract | Clustering time course gene expression data (gene trajectories) is an important step towards solving the complex problem of gene regulatory network (GRN) modeling and discovery as it significantly reduces the dimensionality of the gene space required for analysis. This paper introduces a novel method that hybridizes Genetic Algorithm (GA) and Expectation Maximization algorithms (EM) for clustering with the mixtures of Multiple Linear Regression models (MLRs). The proposed method is applied to cluster gene expression time course data into smaller number of classes based on their trajectory similarities. Its performance and application as a generic clustering method to other complex problems are discussed. | |
| dc.identifier.uri | https://hdl.handle.net/10292/610 | |
| dc.publisher | IEEE | |
| dc.relation.uri | http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1380850&isnumber=30107 | |
| dc.rights | ©2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. | |
| dc.rights.accessrights | OpenAccess | |
| dc.source | IEEE International Conference on Neural Networks, 3, 1669-1674 | |
| dc.title | Gene trajectory clustering with a hybrid genetic algorithm and expectation maximization method | |
| dc.type | Conference Proceedings |