肿瘤微阵列数据的小波模极大值特征提取

本文提出针对肿瘤微阵列数据的小波模极大值特征提取方法。首先求两类数据的Bhattacharyya 距离分布,初 步提取特征基因;接着进行小波分解,在频域上用小波分解高频系数检测基因突变点,低频系数逼近表征原始信号特征;然后 通过理论分析和构建SVM 分类器,经过多次实验选取小波基和尺度,提取特征基因。将该算法应用于数据集(1999 年Golub 所 用ALL 和AML),从中提取了5 个基因,分类测试准确率可达94.12%。可见该算法具有较高的可行性与有效性,能为肿瘤间差 异基因研究提供一定参考。     

Gene expression profiling of HepG2 cells after treatment with black tea polyphenols

This study aimed to determine the effects of black tea polyphenols on gene expression in hepatocellular cancer cells. The total RNA from HepG2 hepatocellular cancer cells treated with black tea polyphenols was subjected to Human 14K cDNA microarray analysis. Real-time PCR and Western blot analysis were conducted to verify microarray data. Black tea polyphenols treatment at the dose of 20 mg/L, 40 mg/L or 80 mg/L for one to three days inhibited the growth of HepG2 cells in a dose and time dependent manner. A total of 48 genes showed more than two-fold change after black tea polyphenols treatment, including 17 upregulated genes and 31 downregulated genes, and they were involved in the regulation of cell growth, cell cycle, apoptosis, signaling, angiogenesis, tumor invasion and metastasis. Real-time PCR analysis of the selected genes showed that their mRNA expression changes were consistent with the microarray data. In addition, Western blot analysis of the selected genes showed that their protein expression changes were consistent with mRNA expression. In conclusion, gene expression profiles provide comprehensive molecular mechanisms by which black tea polyphenols exerts growth inhibition effects on cancer cells. The novel molecular targets identified in this study may be further exploited as therapeutic strategies for hepatocellular cancer.     

Development of oligonucleotide microarray involving plasma polymerized acrylic acid

This paper presents the manufacturing of biochips by using the COOH- derived polymer coating deposited by plasma polymerization of acrylic acid. This technology is based on depositing a thin layer obtained by plasma polymerization of acrylic acid which allows a further covalent immobilization of biomolecules on glass substrates. The plasma power value was optimized to maximize the stability of plasma polymerized acrylic acid (PPAA) coatings in water, which has a very important role for such applications. In order to obtain a covalent immobilization of DNA probes on the PPAA coated surface, the activation protocol of carboxylic function was carried out with the help of N-Hydroxy Succinimide and 1-Ethyl-3-(3-DimethylAminopropyl) Carbodiimide. The efficiency of PPAA coated in microarray applications was compared with two types of commercial slides. Such surfaces have shown very interesting results in terms of relative density of attached DNA probe molecules and signal-to-background ratio measured for target DNA hybridization. Nonspecific DNA bonding measurements showed only a small amount of nonspecific physisorption between the DNA probe and the PPAA-activated surfaces. This work shows that the plasma polymerization technique can be successfully applied to produce a high-quality glass surface for the manufacturing of DNA arrays.     

Characterization of Optimal Aptamer‐Microarray Binding Chemistry and Spacer Design

Compared to conventional protein microarrays, the aptamer microarray is a relatively new and competitive method for analytical applications. The three‐dimensional folded aptamers show excellent binding specificity and affinity, and thus, can be used as an alternative to antibodies. Immobilization and binding effects of aptamers under different combinations of surfaces and spacers for aptamer‐microarray applications are investigated. In addition, the effects of spacers integrated at the terminal position of aptamers on made in‐house and commercially available microarray supports are explored.     

一种基于ZBDD结构的Top—k挖掘算法

研究微阵列数据中挖掘Top—k频繁闭合项集问题,并设计挖掘算法ZDtoP。算法采用ZBDD结构压缩存储数据集,使用自顶向下深度优先搜索策略挖掘项集长度不小于给定值min_l的Top—k频繁闭合项集,并对搜索空间进行有效修剪。通过实例证明该算法是正确有效的。     

Towards improving fuzzy clustering using support vector machine: Application to gene expression data

Recent advancement in microarray technology permits monitoring of the expression levels of a large set of genes across a number of time points simultaneously. For extracting knowledge from such huge volume of microarray gene expression data, computational analysis is required. Clustering is one of the important data mining tools for analyzing such microarray data to group similar genes into clusters. Researchers have proposed a number of clustering algorithms in this purpose. In this article, an attempt has been made in order to improve the performance of fuzzy clustering by combining it with support vector machine (SVM) classifier. A recently proposed real-coded variable string length genetic algorithm based clustering technique and an iterated version of fuzzy C-means clustering have been utilized in this purpose. The performance of the proposed clustering scheme has been compared with that of some well-known existing clustering algorithms and their SVM boosted versions for one simulated and six real life gene expression data sets. Statistical significance test based on analysis of variance (ANOVA) followed by posteriori Tukey-Kramer multiple comparison test has been conducted to establish the statistical significance of the superior performance of the proposed clustering scheme. Moreover biological significance of the clustering solutions have been established.     

Markov blanket-embedded genetic algorithm for gene selection

Microarray technologies enable quantitative simultaneous monitoring of expression levels for thousands of genes under various experimental conditions. This new technology has provided a new way of biological classification on a genome-wide scale. However, predictive accuracy is affected by the presence of thousands of genes many of which are unnecessary from the classification point of view. So, a key issue of microarray data classification is to identify the smallest possible set of genes that can achieve good predictive accuracy. In this study, we propose a novel Markov blanket-embedded genetic algorithm (MBEGA) for gene selection problem. In particular, the embedded Markov blanket-based memetic operators add or delete features (or genes) from a genetic algorithm (GA) solution so as to quickly improve the solution and fine-tune the search. Empirical results on synthetic and microarray benchmark datasets suggest that MBEGA is effective and efficient in eliminating irrelevant and redundant features based on both Markov blanket and predictive power in classifier model. A detailed comparative study with other methods from each of filter, wrapper, and standard GA shows that MBEGA gives a best compromise among all four evaluation criteria, i.e., classification accuracy, number of selected genes, computational cost, and robustness.     

基于过采样技术和随机森林的不平衡微阵列数据分类方法研究

近年来,应用DNA微阵列技术对疾病,尤其是癌症进行诊断,已逐渐成为生物信息学领域的研究热点之一。对比其它的数据载体,微阵列数据通常具有一些独有的特点。针对微阵列数据样本分布不平衡这一特点,提出了一种基于概率分布的过采样技术,通过该技术可以为少数类建立一些合理的伪样本,从而使各类的样本数达到均衡,然后使用随机森林分类器对其进行分类。该方法的有效性和可行性已经在两个标准的微阵列数据集上得到了验证。实验结果显示,与传统的方法相比,该方法可以获得更好的分类性能。     

On biological validity indices for soft clustering algorithms for gene expression data

Unsupervised clustering methods such as K-means, hierarchical clustering and fuzzy c-means have been widely applied to the analysis of gene expression data to identify biologically relevant groups of genes. Recent studies have suggested that the incorporation of biological information into validation methods to assess the quality of clustering results might be useful in facilitating biological and biomedical knowledge discoveries. In this study, we generalize two bio-validity indices, the biological homogeneity index and the biological stability index, to quantify the abilities of soft clustering algorithms such as fuzzy c-means and model-based clustering. The results of an evaluation of several existing soft clustering algorithms using simulated and real data sets indicate that the soft versions of the indices provide both better precision and better accuracy than the classical ones. The significance of the proposed indices is also discussed.