Feature selection and molecular classification of cancer using genetic programming
Feature selection and molecular classification of cancer using genetic programming
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DOI：
10.1593/neo.07121
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发表时间：
2007-04-01
期刊：
影响因子：
4.8
通讯作者：
Chinnaiyan, Arul M.
中科院分区：
文献类型：
--
作者：
Yu, Jianjun;Yu, Jindan;Chinnaiyan, Arul M.
尽管基于微阵列的肿瘤分子分类取得了重要进展，但其在临床环境中的应用仍然令人生畏。这部分是由于目前的分析程序在发现稳健的生物标志物和开发具有实用基因集的分类器方面的限制。遗传编程（GP）是一种机器学习技术，它使用进化算法来模拟自然选择以及种群动态，从而导致简单和可理解的分类器。在这里，我们将GP应用于癌症表达谱数据，以选择特征基因，并通过这些基因的数学整合来构建分子分类器。对前列腺癌数据集生成的数千个GP分类器的分析揭示了一组高度区分性特征基因的重复使用，其中许多已知与疾病相关。GP分类器通常包含五个或更少的基因，并成功预测癌症类型和亚型。更重要的是，在一项研究中生成的GP分类器能够预测来自独立研究的样本，该研究可能使用了不同的微阵列平台。此外，GP产生的分类精度优于或类似于传统的分类方法。此外，GP分类器的数学表达提供了对分类器基因之间的关系的见解。两者合计，我们的研究结果表明，GP可能是有价值的，用于生成有效的分类包含一组实用的基因诊断/预后癌症分类。
Despite important advances in microarray-based molecular classification of tumors, its application in clinical settings remains formidable. This is in part due to the limitation of current analysis programs in discovering robust biomarkers and developing classifiers with a practical set of genes. Genetic programming (GP) is a type of machine learning technique that uses evolutionary algorithm to simulate natural selection as well as population dynamics, hence leading to simple and comprehensible classifiers. Here we applied GP to cancer expression profiling data to select feature genes and build molecular classifiers by mathematical integration of these genes. Analysis of thousands of GP classifiers generated for a prostate cancer data set revealed repetitive use of a set of highly discriminative feature genes, many of which are known to be disease associated. GP classifiers often comprise five or less genes and successfully predict cancer types and subtypes. More importantly, GP classifiers generated in one study are able to predict samples from an independent study, which may have used different microarray platforms. In addition, GP yielded classification accuracy better than or similar to conventional classification methods. Furthermore, the mathematical expression of GP classifiers provides insights into relationships between classifier genes. Taken together, our results demonstrate that GP may be valuable for generating effective classifiers containing a practical set of genes for diagnostic/prognostic cancer classification.