Cancer classification using microarray and layered architecture genetic programming
Author: 
Jung-Yi LinAuthors Info & Claims
Pages 2085 - 2090
Abstract
An important problem of cancer diagnosis and treatment is to distinguish tumors from malignant or benign. Classifying tumors correctly leads us to target specific therapies properly to maximizing efficiency and reducing toxicity. Through the microarray technology, it is possible that monitoring expression in cells for numerous of genes simultaneously. Therefore we are allowed to use potential information hidden in the gene expression data to build a more accurate and more reliable classification model on tumor samples. In this paper we intend to investigate a new approach for cancer classification using genetic programming and microarray gene expression profiles. The layered architecture genetic programming (LAGEP) is applied to build the classification model. Some typical cancer gene expression datasets are validated to demonstrate the classification accuracy of the proposed model.
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Index Terms
- Cancer classification using microarray and layered architecture genetic programming
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Published: 08 July 2009
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