Gene expression analysis for tumor classification using vector quantization
datasetposted on 21.11.2017, 00:23 by Márquez, Edna, Espinosa, Ana María, Berumen, Jaime, Lemaitre, Christian
Gene expression analysis is one of the most important tasks for genomic medicine, using these it is possible to classify tumors, which are directly related with the development of cancer. This paper presents a clustering method for tumor classification, vector quantization, using gene expression profiles from microarrays of mRNA with samples of cervical cancer and normal cervix. Vector quantization is used to divide the space into regions, and the centroids of the regions represent patients with tumors or healthy ones. Also the regions found by the vector quantizer are used as the base for classifying other tumors, that could help in the prognostics of the illness or for finding new groups of tumors. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
Bioinformatics -- CongressesComputational biology -- CongressesComputer vision in medicine -- CongressesComputational biology -- Methods -- CongressesPattern recognition, automated -- Methods -- CongressesGene expression analysisClusteringVector quantizationTumor classification2008conference paper1959.1/63695monash:7855