Intelligent Computing Laboratory, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui 230031, China.
Comput Biol Med. 2010 Oct;40(10):802-10. doi: 10.1016/j.compbiomed.2010.08.003. Epub 2010 Sep 22.
Gene expression data collected from DNA microarray are characterized by a large amount of variables (genes), but with only a small amount of observations (experiments). In this paper, manifold learning method is proposed to map the gene expression data to a low dimensional space, and then explore the intrinsic structure of the features so as to classify the microarray data more accurately. The proposed algorithm can project the gene expression data into a subspace with high intra-class compactness and inter-class separability. Experimental results on six DNA microarray datasets demonstrated that our method is efficient for discriminant feature extraction and gene expression data classification. This work is a meaningful attempt to analyze microarray data using manifold learning method; there should be much room for the application of manifold learning to bioinformatics due to its performance.
从 DNA 微阵列收集的基因表达数据的特点是大量的变量（基因），但只有少量的观察（实验）。在本文中，提出了一种流形学习方法，将基因表达数据映射到低维空间，然后探索特征的内在结构，以便更准确地对微阵列数据进行分类。所提出的算法可以将基因表达数据投影到具有高类内紧密度和类间可分离性的子空间中。在六个 DNA 微阵列数据集上的实验结果表明，我们的方法对于判别特征提取和基因表达数据分类是有效的。这项工作是使用流形学习方法分析微阵列数据的一次有意义的尝试；由于其性能，流形学习在生物信息学中的应用应该有很大的空间。
