New gene selection method for multiclass tumor classification by class centroid

In the analysis of gene expression profiles, the selection of genetic markers and precise diagnosis of cancer type are crucial for successful treatment. The selection of discriminatory genes is critical to improve the accuracy and decrease computational complexity and cost in microarray analysis. In this paper, we developed a new statistical parameter, the suitability score to filter genes which only utilize sample distances from the class centroid. The filtered genes are employed in the nearest centroid classification to classify cancer. To evaluate the performance of the new statistical parameter, the proposed approach is applied to three publicly available microarray datasets. In this paper we demonstrate that the proposed gene selection method is steady in handling classification tasks and is a useful tool for gene selection and mining high dimension data.     

Cancer classification and prediction using logistic regression with Bayesian gene selection

In microarray-based cancer classification and prediction, gene selection is an important research problem owing to the large number of genes and the small number of experimental conditions. In this paper, we propose a Bayesian approach to gene selection and classification using the logistic regression model. The basic idea of our approach is in conjunction with a logistic regression model to relate the gene expression with the class labels. We use Gibbs sampling and Markov chain Monte Carlo (MCMC) methods to discover important genes. To implement Gibbs Sampler and MCMC search, we derive a posterior distribution of selected genes given the observed data. After the important genes are identified, the same logistic regression model is then used for cancer classification and prediction. Issues for efficient implementation for the proposed method are discussed. The proposed method is evaluated against several large microarray data sets, including hereditary breast cancer, small round blue-cell tumors, and acute leukemia. The results show that the method can effectively identify important genes consistent with the known biological findings while the accuracy of the classification is also high. Finally, the robustness and sensitivity properties of the proposed method are also investigated.     

Gene selection for tumor classification using neighborhood rough sets and entropy measures

With the development of bioinformatics, tumor classification from gene expression data becomes an important useful technology for cancer diagnosis. Since a gene expression data often contains thousands of genes and a small number of samples, gene selection from gene expression data becomes a key step for tumor classification. Attribute reduction of rough sets has been successfully applied to gene selection field, as it has the characters of data driving and requiring no additional information. However, traditional rough set method deals with discrete data only. As for the gene expression data containing real-value or noisy data, they are usually employed by a discrete preprocessing, which may result in poor classification accuracy. In this paper, we propose a novel gene selection method based on the neighborhood rough set model, which has the ability of dealing with real-value data whilst maintaining the original gene classification information. Moreover, this paper addresses an entropy measure under the frame of neighborhood rough sets for tackling the uncertainty and noisy of gene expression data. The utilization of this measure can bring about a discovery of compact gene subsets. Finally, a gene selection algorithm is designed based on neighborhood granules and the entropy measure. Some experiments on two gene expression data show that the proposed gene selection is an effective method for improving the accuracy of tumor classification.     

Multiclass cancer classification by using fuzzy support vector machine and binary decision tree with gene selection

We investigate the problems of multiclass cancer classification with gene selection from gene expression data. Two different constructed multiclass classifiers with gene selection are proposed, which are fuzzy support vector machine (FSVM) with gene selection and binary classification tree based on SVM with gene selection. Using F test and recursive feature elimination based on SVM as gene selection methods, binary classification tree based on SVM with F test, binary classification tree based on SVM with recursive feature elimination based on SVM, and FSVM with recursive feature elimination based on SVM are tested in our experiments. To accelerate computation, preselecting the strongest genes is also used. The proposed techniques are applied to analyze breast cancer data, small round blue-cell tumors, and acute leukemia data. Compared to existing multiclass cancer classifiers and binary classification tree based on SVM with F test or binary classification tree based on SVM with recursive feature elimination based on SVM mentioned in this paper, FSVM based on recursive feature elimination based on SVM can find most important genes that affect certain types of cancer with high recognition accuracy.     

Classification and selection of biomarkers in genomic data using LASSO

High-throughput gene expression technologies such as microarrays have been utilized in a variety of scientific applications. Most of the work has been done on assessing univariate associations between gene expression profiles with clinical outcome (variable selection) or on developing classification procedures with gene expression data (supervised learning). We consider a hybrid variable selection/classification approach that is based on linear combinations of the gene expression profiles that maximize an accuracy measure summarized using the receiver operating characteristic curve. Under a specific probability model, this leads to the consideration of linear discriminant functions. We incorporate an automated variable selection approach using LASSO. An equivalence between LASSO estimation with support vector machines allows for model fitting using standard software. We apply the proposed method to simulated data as well as data from a recently published prostate cancer study.     

Selection of interdependent genes via dynamic relevance analysis for cancer diagnosis

Microarray analysis is widely accepted for human cancer diagnosis and classification. However the high dimensionality of microarray data poses a great challenge to classification. Gene selection plays a key role in identifying salient genes from thousands of genes in microarray data that can directly contribute to the symptom of disease. Although various excellent selection methods are currently available, one common problem of these methods is that genes which have strong discriminatory power as a group but are weak as individuals will be discarded. In this paper, a new gene selection method is proposed for cancer diagnosis and classification by retaining useful intrinsic groups of interdependent genes. The primary characteristic of this method is that the relevance between each gene and target will be dynamically updated when a new gene is selected. The effectiveness of our method is validated by experiments on six publicly available microarray data sets. Experimental results show that the classification performance and enrichment score achieved by our proposed method is better than those of other selection methods.     

Selection of relevant genes in cancer diagnosis based on their prediction accuracy

MOTIVATIONS: One of the main problems in cancer diagnosis by using DNA microarray data is selecting genes relevant for the pathology by analyzing their expression profiles in tissues in two different phenotypical conditions. The question we pose is the following: how do we measure the relevance of a single gene in a given pathology? METHODS: A gene is relevant for a particular disease if we are able to correctly predict the occurrence of the pathology in new patients on the basis of its expression level only. In other words, a gene is informative for the disease if its expression levels are useful for training a classifier able to generalize, that is, able to correctly predict the status of new patients. In this paper we present a selection bias free, statistically well founded method for finding relevant genes on the basis of their classification ability. RESULTS: We applied the method on a colon cancer data set and produced a list of relevant genes, ranked on the basis of their prediction accuracy. We found, out of more than 6500 available genes, 54 overexpressed in normal tissues and 77 overexpressed in tumor tissues having prediction accuracy greater than 70% with p-value 相似文献   

A two step method to identify clinical outcome relevant genes with microarray data

With advances in microarray technology, many biomarkers selection approaches have been proposed for cancer diagnosis. Marker sets are selected by scoring genes for how well they can discriminate between different classes of diseases [1-4] or are ranked by significance analysis without reference to classification tasks. However there is a pressing need for methods integrating biological priori knowledge in the gene selection process. In this study, we proposed to identify genes primarily in terms of diagnostic outcome relevance. As gene expression is a combination effect, with the help of SVD, the microarray data is decomposed, the eigenvectors correspond to the biological effect of clinical outcomes are identified. Genes which play important roles in determining this biological effect are detected. Therefore, genes are essentially identified in terms of the strength of association with clinical outcomes and the relationship of genes and clinical outcomes is analyzed. Monte Carlo simulations are then used to fine tune the selected gene set in terms of classification accuracy. The approach was tested on four public data sets. Comparative studies show that the selected genes achieved higher classification accuracies. Graphical analysis visualizes that they have close relationship with the cancer class. Statistical simulation shows that the gene set found by the proposed method is also less variable and comparatively invariant to external influences. The biological relevance of the selected genes is further discussed and validated with the literature study and analysis of biological databases.     

A method of tumor classification based on wavelet packet transforms and neighborhood rough set

Tumor classification is an important application domain of gene expression data. Because of its characteristics of high dimensionality and small sample size (SSS), and a great number of redundant genes not related to tumor phenotypes, various feature extraction or gene selection methods have been applied to gene expression data analysis. Wavelet packet transforms (WPT) and neighborhood rough sets (NRS) are effective tools to extract and select features. In this paper, a novel approach of tumor classification is proposed based on WPT and NRS. First the classification features are extracted by WPT and the decision tables are formed, then the attributes of the decision tables are reduced by NRS. Thirdly, a feature subset with few attributes and high classification ability is obtained. The experimental results on three gene expression datasets demonstrate that the proposed method is effective and feasible.     

Computational selection of distinct class- and subclass-specific gene expression signatures

In this investigation we used statistical methods to select genes with expression profiles that partition classes and subclasses of biological samples. Gene expression data corresponding to liver samples from rats treated for 24 h with an enzyme inducer (phenobarbital) or a peroxisome proliferator (clofibrate, gemfibrozil or Wyeth 14,643) were subjected to a modified Z-score test to identify gene outliers and a binomial distribution to reduce the probability of detecting genes as differentially expressed by chance. Hierarchical clustering of 238 statistically valid differentially expressed genes partitioned class-specific gene expression signatures into groups that clustered samples exposed to the enzyme inducer or to peroxisome proliferators. Using analysis of variance (ANOVA) and linear discriminant analysis methods we identified single genes as well as coupled gene expression profiles that separated the phenobarbital from the peroxisome proliferator treated samples and discerned the fibrate (gemfibrozil and clofibrate) subclass of peroxisome proliferators. A comparison of genes ranked by ANOVA with genes assessed as significant by mixedlinear models analysis [J. Comput. Biol. 8 (2001) 625] or ranked by information gain revealed good congruence with the top 10 genes from each statistical method in the contrast between phenobarbital and peroxisome proliferators expression profiles. We propose building upon a classification regimen comprised of analysis of replicate data, outlier diagnostics and gene selection procedures to utilize cDNA microarray data to categorize subclasses of samples exposed to pharmacologic agents.     

Ensemble gene selection by grouping for microarray data classification

Selecting relevant and discriminative genes for sample classification is a common and critical task in gene expression analysis (e.g. disease diagnostic). It is desirable that gene selection can improve classification performance of learning algorithm effectively. In general, for most gene selection methods widely used in reality, an individual gene subset will be chosen according to its discriminative power. One of deficiencies of individual gene subset is that its contribution to classification purpose is limited. This issue can be alleviated by ensemble gene selection based on random selection to some extend. However, the random one requires an unnecessary large number of candidate gene subsets and its reliability is a problem. In this study, we propose a new ensemble method, called ensemble gene selection by grouping (EGSG), to select multiple gene subsets for the classification purpose. Rather than selecting randomly, our method chooses salient gene subsets from microarray data by virtue of information theory and approximate Markov blanket. The effectiveness and accuracy of our method is validated by experiments on five publicly available microarray data sets. The experimental results show that our ensemble gene selection method has comparable classification performance to other gene selection methods, and is more stable than the random one.     

Gene expression profile class prediction using linear Bayesian classifiers

Due to recent advances in DNA microarray technology, using gene expression profiles, diagnostic category of tissue samples can be predicted with high accuracy. In this study, we discuss shortcomings of some existing gene expression profile classification methods and propose a new approach based on linear Bayesian classifiers. In our approach, we first construct gene-level linear classifiers to identify genes that provide high class-prediction accuracies, i.e., low error rates. After this screening phase, starting with the gene that offers the lowest error rate, we construct a multi-dimensional linear classifier by incorporating next best-performing genes, until the prediction error becomes minimum or 0, if possible. When we compared classification performance of our approach against prediction analysis of microarrays (PAM) and support vector machines (SVM) based approaches, we found that our method outperforms PAM and produces comparable results with SVM. In addition, we observed that the gene selection scheme of PAM could be misleading. Albeit SVM achieves relatively higher prediction performance, it has two major disadvantages: Complexity and lack of insight about important genes. Our intuitive approach offers competing performance and also an efficient means for finding important genes.     

Gene and sample selection using T-score with sample selection

Gene selection from high-dimensional microarray gene-expression data is statistically a challenging problem. Filter approaches to gene selection have been popular because of their simplicity, efficiency, and accuracy. Due to small sample size, all samples are generally used to compute relevant ranking statistics and selection of samples in filter-based gene selection methods has not been addressed. In this paper, we extend previously-proposed simultaneous sample and gene selection approach. In a backward elimination method, a modified logistic regression loss function is used to select relevant samples at each iteration, and these samples are used to compute the T-score to rank genes. This method provides a compromise solution between T-score and other support vector machine (SVM) based algorithms. The performance is demonstrated on both simulated and real datasets with criteria such as classification performance, stability and redundancy. Results indicate that computational complexity and stability of the method are improved compared to SVM based methods without compromising the classification performance.     

An efficient statistical feature selection approach for classification of gene expression data

Classification of gene expression data plays a significant role in prediction and diagnosis of diseases. Gene expression data has a special characteristic that there is a mismatch in gene dimension as opposed to sample dimension. All genes do not contribute for efficient classification of samples. A robust feature selection algorithm is required to identify the important genes which help in classifying the samples efficiently. In order to select informative genes (features) based on relevance and redundancy characteristics, many feature selection algorithms have been introduced in the past. Most of the earlier algorithms require computationally expensive search strategy to find an optimal feature subset. Existing feature selection methods are also sensitive to the evaluation measures. The paper introduces a novel and efficient feature selection approach based on statistically defined effective range of features for every class termed as ERGS (Effective Range based Gene Selection). The basic principle behind ERGS is that higher weight is given to the feature that discriminates the classes clearly. Experimental results on well-known gene expression datasets illustrate the effectiveness of the proposed approach. Two popular classifiers viz. Nave Bayes Classifier (NBC) and Support Vector Machine (SVM) have been used for classification. The proposed feature selection algorithm can be helpful in ranking the genes and also is capable of identifying the most relevant genes responsible for diseases like leukemia, colon tumor, lung cancer, diffuse large B-cell lymphoma (DLBCL), prostate cancer.     

基于深度级联森林的乳腺癌基因数据分类研究

乳腺癌基因数据的分类研究在临床医学上具有重要意义。针对基因数据的结构复杂、高维小样本等特点,提出一种最大相关最小条件冗余和深度级联森林结合的基因数据分类方法。选取博德基因研究所乳腺癌基因表达数据集,共98个数据作为样本,每个样本包含1 213个特征基因。首先对数据进行标准化处理,然后利用最大相关最小条件冗余选取特征子集,最后使用深度级联森林对特征子集进行分类。将随机森林、支持向量机和BP神经网络作为对比方法。结果表明,所提出的最大相关最小条件冗余和深度级联森林结合方法的最佳分类准确率达到93.78%,明显优于其他方法。该方法能有效提高乳腺癌基因数据的分类准确率,对基于基因数据的乳腺癌分类具有重要的理论意义与实用价值。     

An improved electromagnetism-like mechanism algorithm and its application to the prediction of diabetes mellitus

Recently, the use of artificial intelligence based data mining techniques for massive medical data classification and diagnosis has gained its popularity, whereas the effectiveness and efficiency by feature selection is worthy to further investigate. In this paper, we presents a novel method for feature selection with the use of opposite sign test (OST) as a local search for the electromagnetism-like mechanism (EM) algorithm, denoted as improved electromagnetism-like mechanism (IEM) algorithm. Nearest neighbor algorithm is served as a classifier for the wrapper method. The proposed IEM algorithm is compared with nine popular feature selection and classification methods. Forty-six datasets from the UCI repository and eight gene expression microarray datasets are collected for comprehensive evaluation. Non-parametric statistical tests are conducted to justify the performance of the methods in terms of classification accuracy and Kappa index. The results confirm that the proposed IEM method is superior to the common state-of-art methods. Furthermore, we apply IEM to predict the occurrence of Type 2 diabetes mellitus (DM) after a gestational DM. Our research helps identify the risk factors for this disease; accordingly accurate diagnosis and prognosis can be achieved to reduce the morbidity and mortality rate caused by DM.     

相关向量机在肿瘤表达谱分类问题中的应用

基因芯片技术能够检测大量基因的表达水平,在肿瘤研究中得到日益广泛的应用。基于基因芯片表达谱的肿瘤分类诊断是肿瘤表达谱研究的一个热点,肿瘤表达谱分类是一个典型的高维度小样本分类问题,描述一个两步策略的分类方法。在测试的基因表达谱中存在大量的非差异表达冗余基因,通过一个有效的基因预选择策略得到一个较小的候选基因子集,然后建立基于相关向量机的分类预测模型。在4个真实的肿瘤表达谱数据上,与几种不同的方法进行比较,结果显示该方法可以得到更好的分类精度,同时表现出很好的稳定性。     

Selecting significant genes by randomization test for cancer classification using gene expression data

Gene selection is an important task in bioinformatics studies, because the accuracy of cancer classification generally depends upon the genes that have biological relevance to the classifying problems. In this work, randomization test (RT) is used as a gene selection method for dealing with gene expression data. In the method, a statistic derived from the statistics of the regression coefficients in a series of partial least squares discriminant analysis (PLSDA) models is used to evaluate the significance of the genes. Informative genes are selected for classifying the four gene expression datasets of prostate cancer, lung cancer, leukemia and non-small cell lung cancer (NSCLC) and the rationality of the results is validated by multiple linear regression (MLR) modeling and principal component analysis (PCA). With the selected genes, satisfactory results can be obtained.     

Exploring correlations in gene expression microarray data for maximum predictive–minimum redundancy biomarker selection and classification

An important issue in the analysis of gene expression microarray data is concerned with the extraction of valuable genetic interactions from high dimensional data sets containing gene expression levels collected for a small sample of assays. Past and ongoing research efforts have been focused on biomarker selection for phenotype classification. Usually, many genes convey useless information for classifying the outcome and should be removed from the analysis; on the other hand, some of them may be highly correlated, which reveals the presence of redundant expressed information. In this paper we propose a method for the selection of highly predictive genes having a low redundancy in their expression levels. The predictive accuracy of the selection is assessed by means of Classification and Regression Trees (CART) models which enable assessment of the performance of the selected genes for classifying the outcome variable and will also uncover complex genetic interactions. The method is illustrated throughout the paper using a public domain colon cancer gene expression data set.