可识别多种癌症的基因功能模块

在应用基因芯片技术研究包括癌症在内的多因素疾病时, 筛选差异表达基因是最重要的问题之一. 然而, 目前基因芯片研究的样本量较小, 不能完全反映在癌症中广泛的基因表达改变的特点, 导致基于统计学方法筛选的差异表达基因在不同的研究中的重复性很低. 本文通过分析7套癌症数据, 发现每种癌症中都有大范围的功能模块发生了基因表达的改变, 因此这些模块有很强的疾病识别能力. 其中7个功能模块能够有效识别多种癌症的疾病样本, 提示在不同癌症的发生与发展过程中存在着共同的基因表达改变机制. 因此, 功能模块可以作为疾病的功能性的标记来取代在基因芯片应用中难以重复发现的单个基因, 用于研究癌症的核心机制和建立更稳定的诊断分类器.     

癌症基因表达谱挖掘中的特征基因选择算法GA/WV

鉴定癌症表达谱的特征基因集合可以促进癌症类型分类的研究,这也可能使病人获得更好的临床诊断?虽然一些方法在基因表达谱分析上取得了成功,但是用基因表达谱数据进行癌症分类研究依然是一个巨大的挑战,其主要原因在于缺少通用而可靠的基因重要性评估方法。GA/WV是一种新的用复杂的生物表达数据评估基因分类重要性的方法,通过联合遗传算法(GA)和加权投票分类算法(WV)得到的特征基因集合不但适用于WV分类器,也适用于其它分类器?将GA/WV方法用癌症基因表达谱数据集的验证,结果表明本方法是一种成功可靠的特征基因选择方法。     

一种基于类均值的肿瘤基因芯片数据的标准化方法

：分析了当前常用的标准化方法在肿瘤基因芯片中引起错误分类的原因,提出了一种基于类均值的标准化方法．该方法对基因表达谱进行双向标准化,并将标准化过程与聚类过程相互缠绕,利用聚类结果来修正参照表达水平．选取了5组肿瘤基因芯片数据,用层次聚类和K-均值聚类算法在不同的方差水平上分别对常用的标准化和基于类均值的标准化处理后的基因表达数据进行聚类分析比较．实验结果表明,基于类均值的标准化方法能有效提高肿瘤基因表达谱聚类结果的质量．     

多因素癌症诊断分类模型的建立和应用

随着基因组学、蛋白质组学技术的运用,对癌症相关分子标志物的筛选,以及基于多个分子标志物的诊断分类模型,成为近年来研究癌症诊断问题的热门途径。本文介绍了如何从基因芯片和质谱等数据库中筛选有诊断作用的分子标志物,以及构建高敏感性、高特异性诊断分类模型的技术路线;并根据在各类肿瘤中的研究实例,分析各分类算法的特点。     

一种基于支持向量机的自适应肿瘤分类检测算法

根据肿瘤分类检测模型的特点,提出了一种新的算法,该算法结合使用了基因选择和数据抽取的有效方法,并在此基础上使用支持向量机对基因表达数据进行分类或者检测。其中乳腺癌的分类交叉验证结果由88．46％提高到100．0％,急性白血病的也由71．05％提高至100．0％。实验结果说明了这一方法的有效性,为在大量的基因表达数据中提高检测癌症的准确性提出了一种比较通用的方法。     

样条变换偏最小二乘在肝癌数据分类中的应用

肝癌是中国最常见的恶性肿瘤之一。基于肿瘤基因表达谱数据的分析与研究是当今研究的热点,对于癌症的早期诊断、治疗具有十分重要的意义。针对高维小样本基因表达谱数据所显现的变量间严重共线性、类别变量与预测变量的非线性关系,采用了基于样条变换的偏最小二乘回归新技术。首先通过筛选法去除基因表达谱数据中的冗余信息,然后以3次B基样条变换实现非线性基因表达谱数据的线性化重构,随后将重构的矩阵交由偏最小二乘法构建类别变量与预测变量间的关系模型。最后,通过对肝癌肿瘤基因表达谱数据的分析,结果显示此分类模型对数据重构稳健,有效的解决了高维小样本基因表达谱数据间的过拟合和变量间的共线性,具有较高的拟合和分类正确率。     

基于SVM和平均影响值的人肿瘤信息基因提取

基于基因表达谱的肿瘤分类信息基因选取是发现肿瘤特异表达基因、探索肿瘤基因表达模式的重要手段。借助由基因表达谱获得的分类信息进行肿瘤诊断是当今生物信息学领域中的一个重要研究方向,有望成为临床医学上一种快速而有效的肿瘤分子诊断方法。鉴于肿瘤基因表达谱样本数据维数高、样本量小以及噪音大等特点,提出一种结合支持向量机应用平均影响值来寻找肿瘤信息基因的算法,其优点是能够搜索到基因数量尽可能少而分类能力尽可能强的多个信息基因子集。采用二分类肿瘤数据集验证算法的可行性和有效性,对于结肠癌样本集,只需3个基因就能获得100%的留一法交叉验证识别准确率。为避免样本集的不同划分对分类性能的影响,进一步采用全折交叉验证方法来评估各信息基因子集的分类性能,优选出更可靠的信息基因子集。与基它肿瘤分类方法相比,实验结果在信息基因数量以及分类性能方面具有明显的优势。     

基于AR模型的基因芯片数据识别

将自回归模型(AR)模型引入基因芯片数据识别领域,提出了基于自回归模型的时间序列特征提取方法．利用动态时轴弯曲(DTW)作为分类器,在标准的肿瘤基因芯片数据的识别结果表明,本方法能够达到100％的识别率,可以应用于基因芯片数据的识别、分类和基因疾病推断。     

基于邻接矩阵分解的肿瘤亚型特征提取方法

基于肿瘤基因表达谱的肿瘤分类是生物信息学的一个重要研究内容。传统的肿瘤信息特征提取方法大多基于信息基因选择方法,但是在筛选基因时,不可避免的会造成分类信息的流失。提出了一种基于邻接矩阵分解的肿瘤亚型特征提取方法,首先对肿瘤基因表达谱数据构造高斯权邻接矩阵,接着对邻接矩阵进行奇异值分解,最后将分解得到的正交矩阵特征行向量作为分类特征输入支持向量机进行分类识别。采用留一法对白血病两个亚型的基因表达谱数据集进行实验,实验结果证明了该方法的可行性和有效性。     

基于LNMF的癌症基因表达谱数据的特征提取

海量数据的存在是现代信息社会的一大特点,如何在成千上万的基因中有效地选出样本的分类特征对癌症的诊治具有重要意义。采用局部非负矩阵分解方法对癌症基因表达谱数据进行特征提取。首先对基因表达谱数据进行筛选,然后构造局部非负矩阵并对其进行分解得到维数低、能充分表征样本的特征向量,最后用支持向量机对特征向量进行分类。结果表明该方法的可行性和有效性。     

Cancer classification from the gene expression profiles by Discriminant Kernel-PLS

Cancer diagnosis depending on microarray technology has drawn more and more attention in the past few years. Accurate and fast diagnosis results make gene expression profiling produced from microarray widely used by a large range of researchers. Much research work highlights the importance of gene selection and gains good results. However, the minimum sets of genes derived from different methods are seldom overlapping and often inconsistent even for the same set of data, partially because of the complexity of cancer disease. In this paper, cancer classification was attempted in an alternative way of the whole gene expression profile for all samples instead of partial gene sets. Here, the three common sets of data were tested by NIPALS-KPLS method for acute leukemia, prostate cancer and lung cancer respectively. Compared to other conventional methods, the results showed wide improvement in classification accuracy. This paper indicates that sample profile of gene expression may be explored as a better indicator for cancer classification, which deserves further investigation.     

Ensemble dependence model for classification and prediction of cancer and normal gene expression data

MOTIVATION: DNA microarray technologies make it possible to simultaneously monitor thousands of genes' expression levels. A topic of great interest is to study the different expression profiles between microarray samples from cancer patients and normal subjects, by classifying them at gene expression levels. Currently, various clustering methods have been proposed in the literature to classify cancer and normal samples based on microarray data, and they are predominantly data-driven approaches. In this paper, we propose an alternative approach, a model-driven approach, which can reveal the relationship between the global gene expression profile and the subject's health status, and thus is promising in predicting the early development of cancer. RESULTS: In this work, we propose an ensemble dependence model, aimed at exploring the group dependence relationship of gene clusters. Under the framework of hypothesis-testing, we employ genes' dependence relationship as a feature to model and classify cancer and normal samples. The proposed classification scheme is applied to several real cancer datasets, including cDNA, Affymetrix microarray and proteomic data. It is noted that the proposed method yields very promising performance. We further investigate the eigenvalue pattern of the proposed method, and we discover different patterns between cancer and normal samples. Moreover, the transition between cancer and normal patterns suggests that the eigenvalue pattern of the proposed models may have potential to predict the early stage of cancer development. In addition, we examine the effects of possible model mismatch on the proposed scheme.     

Large scale data mining approach for gene-specific standardization of microarray gene expression data

MOTIVATION: The identification of the change of gene expression in multifactorial diseases, such as breast cancer is a major goal of DNA microarray experiments. Here we present a new data mining strategy to better analyze the marginal difference in gene expression between microarray samples. The idea is based on the notion that the consideration of gene's behavior in a wide variety of experiments can improve the statistical reliability on identifying genes with moderate changes between samples. RESULTS: The availability of a large collection of array samples sharing the same platform in public databases, such as NCBI GEO, enabled us to re-standardize the expression intensity of a gene using its mean and variation in the wide variety of experimental conditions. This approach was evaluated via the re-identification of breast cancer-specific gene expression. It successfully prioritized several genes associated with breast tumor, for which the expression difference between normal and breast cancer cells was marginal and thus would have been difficult to recognize using conventional analysis methods. Maximizing the utility of microarray data in the public database, it provides a valuable tool particularly for the identification of previously unrecognized disease-related genes. AVAILABILITY: A user friendly web-interface (http://compbio.sookmyung.ac.kr/~lage/) was constructed to provide the present large-scale approach for the analysis of GEO microarray data (GS-LAGE server).     

Fuzzy J-Means and VNS methods for clustering genes from microarray data

MOTIVATION: In the interpretation of gene expression data from a group of microarray experiments that include samples from either different patients or conditions, special consideration must be given to the pleiotropic and epistatic roles of genes, as observed in the variation of gene coexpression patterns. Crisp clustering methods assign each gene to one cluster, thereby omitting information about the multiple roles of genes. RESULTS: Here, we present the application of a local search heuristic, Fuzzy J-Means, embedded into the variable neighborhood search metaheuristic for the clustering of microarray gene expression data. We show that for all the datasets studied this algorithm outperforms the standard Fuzzy C-Means heuristic. Different methods for the utilization of cluster membership information in determining gene coregulation are presented. The clustering and data analyses were performed on simulated datasets as well as experimental cDNA microarray data for breast cancer and human blood from the Stanford Microarray Database. AVAILABILITY: The source code of the clustering software (C programming language) is freely available from Nabil.Belacel@nrc-cnrc.gc.ca     

Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study

Although prognostic gene expression signatures for survival in early-stage lung cancer have been proposed, for clinical application, it is critical to establish their performance across different subject populations and in different laboratories. Here we report a large, training-testing, multi-site, blinded validation study to characterize the performance of several prognostic models based on gene expression for 442 lung adenocarcinomas. The hypotheses proposed examined whether microarray measurements of gene expression either alone or combined with basic clinical covariates (stage, age, sex) could be used to predict overall survival in lung cancer subjects. Several models examined produced risk scores that substantially correlated with actual subject outcome. Most methods performed better with clinical data, supporting the combined use of clinical and molecular information when building prognostic models for early-stage lung cancer. This study also provides the largest available set of microarray data with extensive pathological and clinical annotation for lung adenocarcinomas.     

Approximate geodesic distances reveal biologically relevant structures in microarray data

MOTIVATION: Genome-wide gene expression measurements, as currently determined by the microarray technology, can be represented mathematically as points in a high-dimensional gene expression space. Genes interact with each other in regulatory networks, restricting the cellular gene expression profiles to a certain manifold, or surface, in gene expression space. To obtain knowledge about this manifold, various dimensionality reduction methods and distance metrics are used. For data points distributed on curved manifolds, a sensible distance measure would be the geodesic distance along the manifold. In this work, we examine whether an approximate geodesic distance measure captures biological similarities better than the traditionally used Euclidean distance. RESULTS: We computed approximate geodesic distances, determined by the Isomap algorithm, for one set of lymphoma and one set of lung cancer microarray samples. Compared with the ordinary Euclidean distance metric, this distance measure produced more instructive, biologically relevant, visualizations when applying multidimensional scaling. This suggests the Isomap algorithm as a promising tool for the interpretation of microarray data. Furthermore, the results demonstrate the benefit and importance of taking nonlinearities in gene expression data into account.     

Improved Microarray-Based Decision Support with Graph Encoded Interactome Data

In the past, microarray studies have been criticized due to noise and the limited overlap between gene signatures. Prior biological knowledge should therefore be incorporated as side information in models based on gene expression data to improve the accuracy of diagnosis and prognosis in cancer. As prior knowledge, we investigated interaction and pathway information from the human interactome on different aspects of biological systems. By exploiting the properties of kernel methods, relations between genes with similar functions but active in alternative pathways could be incorporated in a support vector machine classifier based on spectral graph theory. Using 10 microarray data sets, we first reduced the number of data sources relevant for multiple cancer types and outcomes. Three sources on metabolic pathway information (KEGG), protein-protein interactions (OPHID) and miRNA-gene targeting (microRNA.org) outperformed the other sources with regard to the considered class of models. Both fixed and adaptive approaches were subsequently considered to combine the three corresponding classifiers. Averaging the predictions of these classifiers performed best and was significantly better than the model based on microarray data only. These results were confirmed on 6 validation microarray sets, with a significantly improved performance in 4 of them. Integrating interactome data thus improves classification of cancer outcome for the investigated microarray technologies and cancer types. Moreover, this strategy can be incorporated in any kernel method or non-linear version of a non-kernel method.