Classification of scleroderma and normal biopsy data and identification of possible biomarkers of the disease

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{paul:2006:cibcb,

• author = "Topon Kumar Paul and Hitoshi Iba",
• title = "Classification of scleroderma and normal biopsy data and identification of possible biomarkers of the disease",
• booktitle = "Proceedings of {IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology 2006 (CIBCB2006)}",
• year = "2006",
• pages = "306--311",
• address = "Toronto, Ontario, Canada",
• month = sep # " 28-29",
• organization = "IEEE Computational Intelligence Society",
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming, pattern recognition and classification, scleroderma, biomarkers, majority voting",
• URL = "http://www.iba.k.u-tokyo.ac.jp/~topon/Papers/CIBCB2006.pdf",
• DOI = "doi:10.1109/CIBCB.2006.330951",
• abstract = "Scleroderma is an autoimmune disease of the connective tissues, which thickens and hardens the affected areas. Recently, researchers have found evidence that genes are important factors for this disease, and there exist consistent differences in the patterns of gene expressions of skin biopsies from affected and non-affected individuals. In this paper, we apply genetic programming (GP) on the gene expression data of scleroderma and normal biopsies to evolve the classification rules that can differentiate between them. In these evolved rules, we have found six genes that have differential gene expression levels in scleroderma and normal biopsies and thus individually can classify all the samples correctly. In addition to these genes, we have also found some simple rules containing two or more genes that can classify all the samples perfectly.",
• notes = "broken Nov 2021 http://eldar.mathstat.uoguelph.ca/dashlock/CIBCB2006/home.html",

}

Genetic Programming entries for Topon Kumar Paul Hitoshi Iba

Citations