Abstract
Rapid advances in gene expression microarray technology have enabled to discover molecular markers used for cancer diagnosis, prognosis, and prediction. One computational challenge with using microarray data analysis to create cancer classifiers is how to effectively deal with microarray data which are composed of high-dimensional attributes (p) and low-dimensional instances (n). Gene selection and classifier construction are two key issues concerned with this topics. In this article, we reviewed major methods for computational identification of cancer marker genes based on microarray gene expression data. We concluded that simple methods should be preferred to complicated ones for their interpretability and applicability.
Keywords: Cancer, computational biology, marker genes, microarrays.
Current Bioinformatics
Title:Identification of Marker Genes for Cancer Based on Microarrays Using a Computational Biology Approach
Volume: 9 Issue: 2
Author(s): Xiaosheng Wang
Affiliation:
Keywords: Cancer, computational biology, marker genes, microarrays.
Abstract: Rapid advances in gene expression microarray technology have enabled to discover molecular markers used for cancer diagnosis, prognosis, and prediction. One computational challenge with using microarray data analysis to create cancer classifiers is how to effectively deal with microarray data which are composed of high-dimensional attributes (p) and low-dimensional instances (n). Gene selection and classifier construction are two key issues concerned with this topics. In this article, we reviewed major methods for computational identification of cancer marker genes based on microarray gene expression data. We concluded that simple methods should be preferred to complicated ones for their interpretability and applicability.
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Cite this article as:
Wang Xiaosheng, Identification of Marker Genes for Cancer Based on Microarrays Using a Computational Biology Approach, Current Bioinformatics 2014; 9 (2) . https://dx.doi.org/10.2174/1574893608999140109115649
DOI https://dx.doi.org/10.2174/1574893608999140109115649 |
Print ISSN 1574-8936 |
Publisher Name Bentham Science Publisher |
Online ISSN 2212-392X |
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