Temporal gene expression classification with regularised neural network.
Temporal gene expression classification with regularised neural network.
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DOI：
10.1504/ijbra.2005.008443
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发表时间：
2005-01-01
影响因子：
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通讯作者：
Kelemen, Arpad
中科院分区：
文献类型：
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作者：
Liang, Yulan;Kelemen, Arpad
本文提出了正则化神经网络表征的多个异质性的基因表达的时间动态模式。正则化是为了处理嘈杂，高维的时间过程数据和过拟合问题。我们用一个流行的基因表达数据测试了该模型。该模型的性能相比，其他分类技术，如最近邻，支持向量机，自组织映射。实验结果表明，该模型能够有效地捕捉基因表达时序模式的动态特征，克服了微阵列数据中存在的高噪声水平、高度相关的属性、压倒性的相互作用等复杂特征。
This paper proposes regularised neural networks for characterisation of the multiple heterogeneous temporal dynamic patterns of gene expressions. Regularisation is developed to deal with noisy, high dimensional time course data and overfitting problems. We test the proposed model with a popular gene expression data. The model's performance is compared to other classification techniques, such as Nearest Neighbour, Support Vector Machine, and Self Organised Map. Results show that the proposed model can effectively capture the dynamic feature of gene expression temporal patterns despite the high noise levels, the highly correlated attributes, the overwhelming interactions, and other complex features typically present in microarray data.