Cluster analysis using multivariate normal mixture models to detect differential gene expression with microarray data

DNA microarrays make it possible to study simultaneously the expression of thousands of genes in a biological sample. Univariate clustering techniques have been used to discover target genes with differential expression between two experimental conditions. Because of possible loss of information due to use of univariate summary statistics, it may be more effective to use multivariate statistics. We present multivariate normal mixture model based clustering analyses to detect differential gene expression between two conditions.Deviating from the general mixture model and model-based clustering, we propose mixture models with specific mean and covariance structures that account for special features of two-condition microarray experiments. Explicit updating formulas in the EM algorithm for three such models are derived. The methods are applied to a real dataset to compare the expression levels of 1176 genes of rats with and without pneumococcal middle-ear infection to illustrate the performance and usefulness of this approach. About 10 genes and 20 genes are found to be differentially expressed in a six-dimensional modeling and a bivariate modeling, respectively. Two simulation studies are conducted to compare the performance of univariate and multivariate methods. Depending on data, neither method can always dominate the other. The results suggest that multivariate normal mixture models can be useful alternatives to univariate methods to detect differential gene expression in exploratory data analysis.     

BIC评分贝叶斯网络模型及其应用

针对入侵检测系统漏报率、误报率高的缺点,以贝叶斯信息标准(BIC)评分函数为尺度,结合爬山搜索算法,降低朴素贝叶斯网络模型的强独立性假设,提出更符合实际情形的BIC评分贝叶斯网络模型。对模型进行验证和性能分析,实验结果表明,基于BIC评分函数的贝叶斯网络模型对行为特征渐变的DoS攻击和刺探攻击具有较高识别率。     

TANC-BIC结构学习算法

树扩展朴素贝叶斯分类器(TANC)是应用较广的一种贝叶斯分类器。TANC的分类性能优于朴素贝叶斯分类器(NBC)。现有的TANC结构学习算法是基于相关性分析的,采用互信息测度。贝叶斯信息测度(BIC)在基于打分和搜索的贝叶斯网络结构学习中取得了成功,文中用BIC测度来衡量属性结点之间的相关性,提出了一种新的TANC-BIC结构学习算法。在MBNC实验平台上编程实现了TANC-BIC算法,用分类准确率衡量算法的性能。实验结果表明,TANC-BIC算法是有效的。     

长距离高带宽环境下的TCP拥塞控制

对现行TCP及改进版本在高带宽长距离环境(Fast Long-D istance,FLD)下存在的问题进行了研究。通过理论分析和实验验证比较了高带宽长距离环境下两种TCP拥塞控制算法。实验结果表明,FA ST TCP在快速收敛和吞吐率方面性能优越,而B IC在RTT公平性和TCP友好性方面更为突出。最后,指出了进一步的研究方向。     

高速网络中TCP拥塞控制算法的研究

针对TCP在高速网络中的缺陷,提出了改进的BIC TCP拥塞控制算法。优化算法通过监控链路缓存的变化,调整探索可用带宽过程中的拥塞窗口增加值,当拥塞发生时将慢启动门限和拥塞窗口设为估计带宽和最小RTT乘积,达到降低网络拥塞频率和避免因高速数据流导致缓存区溢出的目的。实验结果表明优化算法的性能在高速网络中得到很大的提高。     

基于音频的电视新闻节目的主题检索和聚类

随着流媒体应用的蓬勃兴起,基于媒体内容的检索和管理逐渐成为当前的学术研究热点。新闻节目作为电视节目的一种常见形式,对其主题进行自动提取检索具有重要的实际意义。该文从电视新闻节目的音频入手,综合应用了播音室语音/非播音室语音分类、说话人转换点检测以及按说话人聚类等多种技术,实现了对电视新闻节目的主题的检索和聚类。实验表明,该文中的方法能够找到新闻节目中96%以上的播音室段落,并对其进行准确归类,显示了这种方法的可行性和潜在价值。     

Type I multivariate zero-inflated Poisson distribution with applications

Motivated from the stochastic representation of the univariate zero-inflated Poisson (ZIP) random variable, the authors propose a multivariate ZIP distribution, called as Type I multivariate ZIP distribution, to model correlated multivariate count data with extra zeros. The distributional theory and associated properties are developed. Maximum likelihood estimates for parameters of interest are obtained by Fisher’s scoring algorithm and the expectation–maximization (EM) algorithm, respectively. Asymptotic and bootstrap confidence intervals of parameters are provided. Likelihood ratio test and score test are derived and are compared via simulation studies. Bayesian methods are also presented if prior information on parameters is available. Two real data sets are used to illustrate the proposed methods. Under both AIC and BIC, our analysis of the two data sets supports the Type I multivariate zero-inflated Poisson model as a much less complex alternative with feasibility to the existing multivariate ZIP models proposed by Li et al. (Technometrics, 29–38, Vol 41, 1999).     

基于小波变换的音频分割

针对滑动变长窗口BIC算法冗余分割点多的问题,提出了基于小波子带平均能量方差和BIC的音频分割算法相结合。该算法用小波子带平均能量方差将连续音频流分割成音频段,然后用改进的滑动变长窗口BIC算法在音频段上检测声学改变点。实验表明,该算法取得了较好的分割效果,与滑动变长窗口的BIC算法相比,该算法的准确率、召回率和综合性能都得了提高。     

Comparison of Penalty Functions for Sparse Canonical Correlation Analysis

Canonical correlation analysis (CCA) is a widely used multivariate method for assessing the association between two sets of variables. However, when the number of variables far exceeds the number of subjects, such in the case of large-scale genomic studies, the traditional CCA method is not appropriate. In addition, when the variables are highly correlated, the sample covariance matrices become unstable or undefined. To overcome these two issues, sparse canonical correlation analysis (SCCA) for multiple data sets has been proposed using a Lasso type of penalty. However, these methods do not have direct control over the sparsity of the solution. An additional step that uses a Bayesian Information Criterion (BIC) has also been suggested to further filter out unimportant features. In this paper, a comparison of four penalty functions (Lasso, Elastic-net, smoothly clipped absolute deviation (SCAD), and Hard-threshold) for SCCA with and without the BIC filtering step have been carried out using both real and simulated genotypic and mRNA expression data. This study indicates that the SCAD penalty with a BIC filter would be a preferable penalty function for application of SCCA to genomic data.     

A novel neural network ensemble architecture for time series forecasting

We propose a novel homogeneous neural network ensemble approach called Generalized Regression Neural Network (GEFTS–GRNN) Ensemble for Forecasting Time Series, which is a concatenation of existing machine learning algorithms. GEFTS uses a dynamic nonlinear weighting system wherein the outputs from several base-level GRNNs are combined using a combiner GRNN to produce the final output. We compare GEFTS with the 11 most used algorithms on 30 real datasets. The proposed algorithm appears to be more powerful than existing ones. Unlike conventional algorithms, GEFTS is effective in forecasting time series with seasonal patterns.