基于支持向量机的微阵列基因表达数据分析方法

DNA微阵列技术，使人们可以同时观测成千上万个基因的表达水平，对其数据的分析已成为生物信息学研究的焦点．针对微阵列基因表达数据维数高、样本小、非线性的特点，设计了一种基于支持向量机的基因表达数据分类识别方法，该方法采用信噪比进行基因特征提取，运用支持向量机的不同核函数进行性能测试，针对几个典型数据集的实验表明其识别效果良好．     

用于微阵列分类的Huberized多类支持向量机

提出了一种能同时进行基因选择和微阵列分类的新型多类支持向量机. 通过结合huberized hinge 损失函数与弹性网络惩罚, 所提支持向量机能自动地进行基因选择并激励一种群体效应. 所提支持向量机的系数路关于单正则化参数是分段线性的, 并基于此发展了解路算法, 减少了计算的复杂性. 白血病数据集上的实验验证了所提方法的有效性.     

SVM在基因微阵列癌症数据分类中的应用

在总结二分类支持向量机应用的基础上,提出了利用t-验证方法和Wilcoxon验证方法进行特征选取,以支持向量机（SVM）为分类器,针对基因微阵列癌症数据进行分析的新方法,通过对白血病数据集和结肠癌数据集的分类实验,证明提出的方法不但识别率高,而且需要选取的特征子集小,分类速度快,提高了分类的准确性与分类速度。     

SVM图像分割方法的研究

支持向量机分类方法已应用于图像分割。本文以彩色图像分割为例,通过对支持向量机图像分割方法和基于灰度直方图图像分割方法进行比较研究。研究揭示:支持向量机图像分割方法是一种在SVM图像上的全局门限分割,并能自动获得默认门限值。使用支持向量机图像方法的这一特点,很容易使其它的分割方法与支持向量机方法相结合,产生新的混和方法。     

基于统计学习理论的支持向量机的分类方法

支持向量机是一种新型机器学习方法,由于其出色的学习性能,该技术已成为机器学习领域新的研究热点。介绍用于分类的支持向量机的统计学习理论基础,在此基础上提出了支持向量机的分类算法,讨论了支持向量机存在的问题,对用于分类的支持向量机的应用前景进行了展望。     

支持向量机的研究进展

支持向量机是一种新的机器学习方法。对于支持向量机的算法、模型的选择及支持向量机的扩展进行了阐述及总结．并提出支持向量机的发展趋势和研究方向。     

基于统计学习理论的支持向量机的分类方法

支持向量机是一种新型机器学习方法，由于其出色的学习性能，该技术已成为机器学习领域新的研究热点。介绍用于分类的支持向量机的统计学习理论基础，在此基础上提出了支持向量机的分类算法，讨论了支持向量机存在的问题，对用于分类的支持向量机的应用前景进行了展望。     

支持向量机训练和实现算法综述

支持向量机是在统计学习理论基础上发展起来的一种新的机器学习方法,支持向量机已成为目前研究的热点,并在模式识别、回归分析、函数估计等领域有了广泛的应用。该文在介绍了支持向量机的目前研究、应用状况和新进展的基础上,对支持向量机训练和实现算法进行了综述,最后指出了进一步研究和应用亟待解决的一些问题。     

基于支持向量机的非线性系统辨识研究*

研究了基于支持向量机的非线性系统辨识方法并进行了仿真试验,用交叉验证的方法进行支持向量机参数选择。有效的仿真结果表明支持向量机是非线性系统辨识的一种非常有效的方法。     

基于支持向量机方法的多目标图像分割

支持向量机方法被看作是对传统学习分类方法的一个好的替代,特别在小训练样本、高维情况下,具有较好的泛化性能。该文采用了支持向量机方法对多目标图像进行了分割研究。实验结果表明:模型参数对支持向量机方法的分割性能有较大的影响;对多目标图像的分割,支持向量机方法是一种很有前景的分割技术。     

Classification of gene functions using support vector machine for time-course gene expression data

Since most biological systems are developmental and dynamic, time-course gene expression profiles provide an important characterization of gene functions. Assigning functions for genes with unknown functions based on time-course gene expressions is an important task in functional genomics. Recently, various methods have been proposed for the classification of gene functions based on time-course gene expression data. In this paper, we consider the classification of gene functions from functional data analysis viewpoint, where a functional support vector machine is adopted. The functional support vector machine can model temporal effects of time-course gene expression data by incorporating the coefficients as well as the basis matrix obtained from a finite expansion of gene expressions on a set of basis functions. We apply the functional support vector machine to both real microarray and simulated data. Our results indicate that the functional support vector machine is effective in discriminating gene functions of time-course gene expressions with predefined functions. The method also provides valuable functional information about interactions between genes and allows the assignment of new functions to genes with unknown functions.     

Applying 1-norm SVM with squared loss to gene selection for cancer classification

Gene selection methods available have high computational complexity. This paper applies an 1-norm support vector machine with the squared loss (1-norm SVMSL) to implement fast gene selection for cancer classification. The 1-norm SVMSL, a variant of the 1-norm support vector machine (1-norm SVM) has been proposed. Basically, the 1-norm SVMSL can perform gene selection and classification at the same. However, to improve classification performance, we only use the 1-norm SVMSL as a gene selector, and adopt a subsequent classifier to classify the selected genes. We perform extensive experiments on four DNA microarray data sets. Experimental results indicate that the 1-norm SVMSL has a very fast gene selection speed compared with other methods. For example, the 1-norm SVMSL is almost an order of magnitude faster than the 1-norm SVM, and at least four orders of magnitude faster than SVM-RFE (recursive feature elimination), a state-of-the-art method.     

New normalization methods using support vector machine quantile regression approach in microarray analysis

There are many sources of systematic variations in cDNA microarray experiments which affect the measured gene expression levels. Print-tip lowess normalization is widely used in situations where dye biases can depend on spot overall intensity and/or spatial location within the array. However, print-tip lowess normalization performs poorly in situations where error variability for each gene is heterogeneous over intensity ranges. We first develop support vector machine quantile regression (SVMQR) by extending support vector machine regression (SVMR) for the estimation of linear and nonlinear quantile regressions, and then propose some new print-tip normalization methods based on SVMR and SVMQR. We apply our proposed normalization methods to previous cDNA microarray data of apolipoprotein AI-knockout (apoAI-KO) mice, diet-induced obese mice, and genistein-fed obese mice. From our comparative analyses, we find that our proposed methods perform better than the existing print-tip lowess normalization method.     

Stepwise feature selection using generalized logistic loss

Microarray experiments have raised challenging questions such as how to make an accurate identification of a set of marker genes responsible for various cancers. In statistics, this specific task can be posed as the feature selection problem. Since a support vector machine can deal with a vast number of features, it has gained wide spread use in microarray data analysis. We propose a stepwise feature selection using the generalized logistic loss that is a smooth approximation of the usual hinge loss. We compare the proposed method with the support vector machine with recursive feature elimination for both real and simulated datasets. It is illustrated that the proposed method can improve the quality of feature selection through standardization while the method retains similar predictive performance compared with the recursive feature elimination.     

Wavelet-based feature extraction for DNA microarray classification

Complementary DNA (cDNA) microarray-based tumor gene expression profiles have been successfully used for cancer diagnosis. The main difficulty in processing cDNA microarrays is the ultra-high dimensionality of the microarrays. In this paper, we approach the dimensionality reduction using a novel wavelet-based approach that extracts classification features through microarray-block processing, thresholding, and averaging of approximation coefficients. The proposed cancer detection system presents the extracted features to a support vector machine SVM for classification (tumor or non-tumor). To show the robustness of the proposed system, its performance is tested on two public cancer microarray databases.     

SVM在基因表达数据分类中的研究和应用

介绍了一种使用基因芯片实验产生的基因表达数据对功能基因进行分类的方法,该方法是以支持向量机(SVM)理论为基础的。文中描述了径向基函数SVM,与其它SVM相比,径向基函数SVM在基因分类中有更好的性能。SVM的理论基础是统计学习理论,它不仅结构简单,而且技术性能高,泛化能力强,在基因表达式分类中表现出有很多优点,成为热点研究方向。     

Advanced machine learning techniques for microarray spot quality classification

It is well known that microarray printing, hybridization, and washing oftentimes create erroneous measurements, and these errors detrimentally impact machine microarray spot quality classification. Thus, it is crucial to identify and remove these errors if automation is to replace the still common practice of visually assessing spot quality, an extremely expensive and time-consuming procedure. A major problem in microarray spot quality classification methods proposed in the literature is the correlation among the features extracted from the spots. In this paper, we propose using a random subspace ensemble of neural networks and a feature selection algorithm to improve the performance of our microarray spot quality classification method. Our best method obtains an error under the receiver operating characteristic curve (EAUR) of 0.3 outperforming the stand-alone support vector machine EAUR of 1.7. The consistency of our proposed approach makes it a viable alternative to the labour-intensive manual method of spot quality assessment.     

基于支持向量机分类水平集方法的图像特征提取研究

本文提出了一种基于支持向量机分类器的水平集方法的图像特征提取方法,在提取图像特征的水平集方法应用中,通过改进水平集能量函数,引入区域信息,能够同时提取图像目标的内外边界,并且收敛速度快。提取图像特征之后通过支持向量机非线性分类器对待处理图像进行分类。