应用DNA芯片数据挖掘复杂疾病相关基因的集成决策方法

DNA芯片技术的迅速发展, 可同时检测成千上万个基因的表达谱数据, 为生命科学家们从一个全新的角度阐明生命的本质提供了可能性. 目前, 基因表达谱分析的工作大多集中在对癌症等疾病分类、疾病亚型识别等方面, 而从这些基因表达谱信息中挖掘反映疾病本质特征的相关基因, 是一项在后基因组时代更具挑战意义的科学研究, 基因挖掘由于缺少理想的数据挖掘技术而被忽视. 我们提出了一种新颖的特征基因挖掘的集成决策方法, 目的在于解决三个重要的生物学问题: 生物学分类及疾病分型、复杂疾病相关基因深度挖掘和目标驱使的基因网络构建. 我们成功地将此集成决策方法应用于一套结肠癌DNA表达谱数据, 结果显示这一新颖的特征基因挖掘技术在应用DNA芯片数据分析、挖掘复杂疾病相关基因等方面具有很高的价值.     

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。     

GO功能类与基因差异表达的关联规则挖掘算法

针对基因功能分类体系Gene Ontology的层次结构特点,修改关联规则挖掘算法Apriori,开发“挖掘与基因差异表达关联的GO功能组合”软件(RuleGO).RuleGO以基因表达谱上的差异表达基因集合和不差异表达基因集合为输入,输出组合特征功能类与基因差异表达现象的关联规则,有助于解释基因差异表达现象的本质原因,如疾病发病机制、药物作用机理等.将RuleGO 和OntoExpress应用在结肠癌和腺癌表达谱数据集上,结果显示,RuleGO比OntoExpress能发现更多的与差异表达现象关联的特征功能类,更能看到在OntoExpress上不能发现的组合特征功能类.另外,结果显示,将规则的置信度和支持度要求设置较高时,一般只有组合功能类才能满足要求,这提示在基因表达谱分析中不宜采用单个角度的单个功能分类单元,考虑功能分类单元的组合可能更有意义.     

基于决策森林特征基因的两种识别方法

应用DNA芯片可获得成千上万个基因的表达谱数据。寻找对疾病有鉴别力的特征基因 ,滤掉与疾病无关的基因是基因表达谱数据分析的关键问题。利用决策森林方法的集成优势 ,提出基于决策森林的两种特征基因识别方法。该方法先由决策森林按照一定的显著性水平滤掉大部分与疾病类别无关的基因 ,然后采用统计频数法和扰动法 ,根据所选特征对分类的贡献程度对初选的特征基因作更加精细地选择。最后 ,选用神经网络作为外部分类器对所选的特征基因子集进行评价 ,将提出的方法应用于 4 0例结肠癌组织与 2 2例正常组织中 2 0 0 0个基因的表达谱实验数据。结果表明 :上述两种方法选出的特征基因均具有较高的疾病鉴别能力 ,均可获得最优特征基因子集 ,基于决策森林的统计频数法优于扰动法。     

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。     

基于基因表达谱的肿瘤特异基因表达模式研究

基于肿瘤基因表达谱, 利用生物信息学的方法, 从肿瘤与正常组织的样本分类入手就肿瘤特异表达基因的发现及其表达模式问题进行了分析和研究, 进而探讨了肿瘤在基因表达上的特点. 首先, 在分析肿瘤基因表达谱特点的基础上, 提出了基于Relief算法的样本分类特征基因选取策略; 然后, 以支持向量机为分类工具进行样本类型的识别, 以分类错误率为标准选取样本分类特征基因, 并对其中反映肿瘤与正常样本组织构成特点的组织特异表达基因进行排除以突出肿瘤样本真实的类别特征; 最后结合统计学方法, 从信息学的角度论证了分类特征基因在肿瘤组织中特异表达的确实性和普遍性, 并对这些基因在肿瘤组织中呈现出的特异的表达模式进行了分析.     

基因标签区分癌症样本的初步分析

从基因表达水平的角度对癌症样本进行分子分型,其较高的临床结果预测能力已经获得越来越广泛的关注。研究发现,某些基因集合(称为基因标签)的表达水平可以显著区分不同癌症样本之间的亚型差异并与癌症病人的临床结果显著关联。使用主成分分析法能够帮助我们找出这类表达谱与临床结果密切相关的基因标签。本研究通过主成分分析法从MSig BD和GeneSigDB数据库包含的基因集中找出可以解释不同癌症样本区别的基因标签并进一步评价了这些基因标签对癌症病人生存结果的预测能力。通过对10种癌症分别进行检验,我们找到了数量可观的可以区分癌症样本且相应的临床结果具有显著差异的基因标签。基于基因表达谱、临床数据和主成分分析法,基因标签能够对具有不同临床结果的癌症样本进行亚类的区分。     

基于逐步提取偏最小二乘主成分的特征选择方法

特征选择技术被广泛应用于生物信息学中。通过重复利用偏最小二乘（partial least square,PLS）方法提取主成分,通过逐次选择在主成分中权重较大的基因,将PLS应用于特征选择中。将这种方法用于对肿瘤基因表达谱数据的特征基因选择中,并用提取的特征基因分类,用8个特征基因进行分类时,能达到92.5%的正确率。     

根据GO分类体系选择与实验条件相关的差异表达功能结点进行基因功能深化预测

利用有限个实验条件下的基因表达谱数据,只能对与实验条件相关的基因功能类进行有效预测,所以有必要限定可预测的基因功能类范围。据此,首先基于GeneOntology(GO)选择富集差异表达基因与实验条件相关的功能类。再通过支持向量机分类器,深化预测迄今只注释到实验条件相关功能类的父结点的基因是否属于该实验条件相关功能类。应用于一套酵母基因表达谱数据,结果显示,在剔除了高度不平衡的训练集合后,平均真阳性率(precision)与平均覆盖率(recall)都分别达到了71%与47%以上。     

随机森林：一种重要的肿瘤特征基因选择法

特征选择技术已经被广泛地应用于生物信息学科,随机森林（random forests,RF）是其中一种重要的特征选择方法。利用RF对胃癌、结肠癌和肺癌等5组基因表达谱数据进行特征基因选择,将选择结果与支持向量机（support vector machine,SVM）结合对原数据集分类,并对特征基因选择及分类结果进行初步的分析。同时使用微阵列显著性分析（significant analysis of microarray,SAM）和ReliefF法与RF比较,结果显示随机森林选择的特征基因包含更多分类信息,分类准确率更高。结合该方法自身具有的分类方面的诸多优势,随机森林可以作为一种可靠的基因表达谱数据分析手段被广泛使用。     

基于多分类支持向量机的基因表达系列分析

基因表达系列分析(Serial analysis of gene expression,SAGE)是一种基因表达数据,反映了细胞内的动态变化。模式识别和可视化方法是分析SAGE数据的基本工具,但是由于缺乏描述数据的统计特性,传统的聚类分析技术不适用于SAGE数据的分析。本文提出了一种基于多分类和支持向量机的SAGE数据的分析法。经过对模拟数据和人类癌症SAGE数据的分析,基于径向基核函数的多分类支持向量机算法"一对一"(one-against-one,OAO)算法提供了比PoissonC和PoissonS更好的分类结果。     

Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method.

MOTIVATION: We recently introduced a multivariate approach that selects a subset of predictive genes jointly for sample classification based on expression data. We tested the algorithm on colon and leukemia data sets. As an extension to our earlier work, we systematically examine the sensitivity, reproducibility and stability of gene selection/sample classification to the choice of parameters of the algorithm. METHODS: Our approach combines a Genetic Algorithm (GA) and the k-Nearest Neighbor (KNN) method to identify genes that can jointly discriminate between different classes of samples (e.g. normal versus tumor). The GA/KNN method is a stochastic supervised pattern recognition method. The genes identified are subsequently used to classify independent test set samples. RESULTS: The GA/KNN method is capable of selecting a subset of predictive genes from a large noisy data set for sample classification. It is a multivariate approach that can capture the correlated structure in the data. We find that for a given data set gene selection is highly repeatable in independent runs using the GA/KNN method. In general, however, gene selection may be less robust than classification. AVAILABILITY: The method is available at http://dir.niehs.nih.gov/microarray/datamining CONTACT: LI3@niehs.nih.gov     

遗传优化算法在基因数据分类中的应用

本文提出了一种基于遗传算法的基因微阵列数据特征提取方法。首先对原始数据进行标准化,然后利用方差分析方法对数据进行降低维数处理,最后利用遗传算法对数据进行优化。针对基因数据对遗传算子和适应度函数进行设置,优化数据集选取特征基因,得到较小的特征子集。为了验证选取的特征,利用样本划分法通过判别分析建立分类器进行判定。实验论证此方法具有理想的分类效果,算法稳定、效率高。     

Co-ABC: Correlation artificial bee colony algorithm for biomarker gene discovery using gene expression profile

In this paper, we propose a new hybrid method based on Correlation-based feature selection method and Artificial Bee Colony algorithm,namely Co-ABC to select a small number of relevant genes for accurate classification of gene expression profile. The Co-ABC consists of three stages which are fully cooperated: The first stage aims to filter noisy and redundant genes in high dimensionality domains by applying Correlation-based feature Selection (CFS) filter method. In the second stage, Artificial Bee Colony (ABC) algorithm is used to select the informative and meaningful genes. In the third stage, we adopt a Support Vector Machine (SVM) algorithm as classifier using the preselected genes form second stage. The overall performance of our proposed Co-ABC algorithm was evaluated using six gene expression profile for binary and multi-class cancer datasets. In addition, in order to proof the efficiency of our proposed Co-ABC algorithm, we compare it with previously known related methods. Two of these methods was re-implemented for the sake of a fair comparison using the same parameters. These two methods are: Co-GA, which is CFS combined with a genetic algorithm GA. The second one named Co-PSO, which is CFS combined with a particle swarm optimization algorithm PSO. The experimental results shows that the proposed Co-ABC algorithm acquire the accurate classification performance using small number of predictive genes. This proofs that Co-ABC is a efficient approach for biomarker gene discovery using cancer gene expression profile.     

Selecting a minimal number of relevant genes from microarray data to design accurate tissue classifiers

It is essential to select a minimal number of relevant genes from microarray data while maximizing classification accuracy for the development of inexpensive diagnostic tests. However, it is intractable to simultaneously optimize gene selection and classification accuracy that is a large parameter optimization problem. We propose an efficient evolutionary approach to gene selection from microarray data which can be combined with the optimal design of various multiclass classifiers. The proposed method (named GeneSelect) consists of three parts which are fully cooperated: an efficient encoding scheme of candidate solutions, a generalized fitness function, and an intelligent genetic algorithm (IGA). An existing hybrid approach based on genetic algorithm and maximum likelihood classification (GA/MLHD) is proposed to select a small number of relevant genes for accurate classification of samples. To evaluate the performance of GeneSelect, the gene selection is combined with the same maximum likelihood classification (named IGA/MLHD) for convenient comparisons. The performance of IGA/MLHD is applied to 11 cancer-related human gene expression datasets. The simulation results show that IGA/MLHD is superior to GA/MLHD in terms of the number of selected genes, classification accuracy, and robustness of selected genes and accuracy.     

Multiclass cancer classification and biomarker discovery using GA-based algorithms

MOTIVATION: The development of microarray-based high-throughput gene profiling has led to the hope that this technology could provide an efficient and accurate means of diagnosing and classifying tumors, as well as predicting prognoses and effective treatments. However, the large amount of data generated by microarrays requires effective reduction of discriminant gene features into reliable sets of tumor biomarkers for such multiclass tumor discrimination. The availability of reliable sets of biomarkers, especially serum biomarkers, should have a major impact on our understanding and treatment of cancer. RESULTS: We have combined genetic algorithm (GA) and all paired (AP) support vector machine (SVM) methods for multiclass cancer categorization. Predictive features can be automatically determined through iterative GA/SVM, leading to very compact sets of non-redundant cancer-relevant genes with the best classification performance reported to date. Interestingly, these different classifier sets harbor only modest overlapping gene features but have similar levels of accuracy in leave-one-out cross-validations (LOOCV). Further characterization of these optimal tumor discriminant features, including the use of nearest shrunken centroids (NSC), analysis of annotations and literature text mining, reveals previously unappreciated tumor subclasses and a series of genes that could be used as cancer biomarkers. With this approach, we believe that microarray-based multiclass molecular analysis can be an effective tool for cancer biomarker discovery and subsequent molecular cancer diagnosis.     

基于信号稀疏表示的高光谱散射图像苹果粉质化分类

苹果的粉质化是指苹果果肉发软、汁液减少等一系列物理和生理变化现象,采用高光谱散射图像技术结合信号稀疏表示分类算法(SRSA)研究了苹果的粉质化分类问题。首先利用平均反射算法(MEAN)提取了600～1000 nm的高光谱散射图像特征;引入遗传算法(GA)解决分类样本的不均衡问题,在此基础上,把苹果的粉质化分类问题,转化为一个求解待识别样本对于整体训练样本的稀疏表示问题。仿真结果表明,基于信号稀疏表示分类算法的苹果粉质化分类精度为79.8%,高于偏最小二乘判别分析(PLSDA)的74.8%,为苹果的粉质化分类提供了一种新的有效的方法。     

Genetic algorithms applied to multi-class prediction for the analysis of gene expression data

MOTIVATION: An important challenge in the use of large-scale gene expression data for biological classification occurs when the expression dataset being analyzed involves multiple classes. Key issues that need to be addressed under such circumstances are the efficient selection of good predictive gene groups from datasets that are inherently 'noisy', and the development of new methodologies that can enhance the successful classification of these complex datasets. METHODS: We have applied genetic algorithms (GAs) to the problem of multi-class prediction. A GA-based gene selection scheme is described that automatically determines the members of a predictive gene group, as well as the optimal group size, that maximizes classification success using a maximum likelihood (MLHD) classification method. RESULTS: The GA/MLHD-based approach achieves higher classification accuracies than other published predictive methods on the same multi-class test dataset. It also permits substantial feature reduction in classifier genesets without compromising predictive accuracy. We propose that GA-based algorithms may represent a powerful new tool in the analysis and exploration of complex multi-class gene expression data. AVAILABILITY: Supplementary information, data sets and source codes are available at http://www.omniarray.com/bioinformatics/GA.