From pattern to causality: using linear discriminant analysis and Bayesian network on microarray data of breast cancers
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In this paper, we aim at using genetic algorithms for gene selection and propose silhouette statistics as a discriminant function to classify breast cancers on microarray data for pattern discovery. In order to see the causality among these genes, we use the Bayesian method to construct a probability network for the pattern discovered. Consequently, we found a set of genes that is effective to discriminate breast cancer subtypes and present their probability dependencies to construct a diagnostic system. 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) ;
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Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
本研究旨在利用遗传算法（genetic algorithms）开展基因选择工作，并提出以轮廓统计量（silhouette statistics）作为判别函数，基于微阵列数据（microarray data）对乳腺癌进行分类以实现模式发现。为探究上述基因间的因果关联，本研究采用贝叶斯方法（Bayesian method）为所发现的模式构建概率网络。最终，本研究筛选出一组可有效区分乳腺癌亚型的基因，并基于其概率依赖关系构建了诊断系统。相关成果收录于2008年第三届IAPR生物信息学模式识别国际会议（Third IAPR International Conference on Pattern Recognition in Bioinformatics，简称PRIB）论文集，获取链接：http://dx.doi.org/10.1007/978-3-540-88436-1。
贡献方：莫纳什大学（Monash University）信息技术学院吉普斯兰信息技术分校；切蒂，马杜；艾哈迈德，尚达尔；恩戈姆，阿利乌内；滕，石伟；第三届IAPR生物信息学模式识别国际会议（PRIB 2008，澳大利亚墨尔本，2008年）。
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版权声明：本内容版权归第三届IAPR生物信息学模式识别国际会议所有，保留所有权利。
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Monash University
