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A Copula-Based Algorithm for Discovering Patterns of Dependent Observations

JOURNAL OF CLASSIFICATION 2012年第1期29卷 50-75页

作者： Di Lascio, F. Marta L. Giannerini, Simone Univ Bologna Dipartimento Sci Stat I-40126 Bologna Italy

The main aim of this work is the study of clustering dependent data by means of copula functions. Copulas are popular multivariate tools whose importance within clustering methods has not been investigated yet in detail. We propose a new algorithm (CoClust in brief) that allows to cluster dependent data according to the multivariate structure of the generating process without any assumption on the margins. Moreover, the approach does not require either to choose a starting classification or to set a priori the number of clusters;in fact, the CoClust selects them by using a criterion based on the log-likelihood of a copula fit. We test our proposal on simulated data for different dependence scenarios and compare it with a model-based clustering technique. Finally, we show applications of the CoClust to real microarray data of breast-cancer patients.

关键词： Clustering methods CoClust algorithm Copula functions Model-based clustering microarray data

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Feature Selection for microarray data by AUC Analysis

Feature Selection for Microarray Data by AUC Analysis

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IEEE International Conference on System, Man, and Cybernetic

作者： Canul-Reich, Juana Hall, Lawrence O. Goldgof, Dmitry Eschrich, Steven A. Univ S Florida Dept Comp Sci & Engn Tampa FL 33620 USA H Lee Moffitt Cancer Ctr Dept Biomed Informat Tampa FL 33612 USA Res Inst Tampa FL 33612 USA

ISBN: (纸本)9781424423835

microarray datasets are often limited to a small number of samples with a large number of gene expressions. Therefore, dimensionality reduction through a feature/gene selection process is highly important for classification purposes. In this paper, a feature perturbation method we previously introduced is applied to do gene selection from microarray data. A publicly available colon cancer dataset is used in our experiments. In comparison with SVM-RFE, our method is better with feature sets of between 10 and 80, however for less than 10 features SVM-RFE results in higher accuracy. An analysis of the area under the curve of the feature perturbation method for the top 50 and 25 features is performed, aiming to determine the proper amount of noise to be applied. We show that a good set of small features/genes can be found using the feature perturbation method.

关键词： microarray data classification support vector machines feature selection

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MODELING DEPENDENT GENE EXPRESSION

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ANNALS OF APPLIED STATISTICS 2012年第2期6卷 542-560页

作者： Telesca, Donatello Mueller, Peter Parmigiani, Giovanni Freedman, Ralph S. UCLA Sch Public Hlth Dept Biostat Los Angeles CA 90095 USA Harvard Univ Sch Publ Hlth Dept Biostat Boston MA 02115 USA Univ Texas Austin Dept Math Austin TX 78712 USA Univ Texas MD Anderson Canc Ctr Dept Gynecol Oncol Houston TX 77030 USA

In this paper we propose a Bayesian approach for inference about dependence of high throughput gene expression. Our goals are to use prior knowledge about pathways to anchor inference about dependence among genes;to account for this dependence while making inferences about differences in mean expression across phenotypes;and to explore differences in the dependence itself across phenotypes. Useful features of the proposed approach are a model-based parsimonious representation of expression as an ordinal outcome, a novel and flexible representation of prior information on the nature of dependencies, and the use of a coherent probability model over both the structure and strength of the dependencies of interest. We evaluate our approach through simulations and in the analysis of data on expression of genes in the Complement and Coagulation Cascade pathway in ovarian cancer.

关键词： Conditional independence microarray data probability of expression probit models reciprocal graphs reversible jumps MCMC

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Combining Singular Value Decomposition and t-test into Hybrid Approach for Significant Gene Extraction from microarray data

Combining Singular Value Decomposition and t-test into Hybri...

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8th IEEE International Conference on Bioinformatics and Bioengineering

作者： Alshalalfa, Mohammed Alhajj, Reda Rokne, Jon Univ Calgary Dept Comp Sci Calgary AB T2N 1N4 Canada

ISBN: (纸本)9781424428441

Significant gene extraction from microarray data is a challenging problem which is of great interest to researchers in Computational Biology, Medicine, Computer Science and Statistics. A number of methods have been proposed for extracting the smallest number of genes which can accurately classify different samples. Most of these methods ignore the fact that microarray data is mostly noisy. For instance, only using a statistical t-test has been shown to be insufficient since it result in a high false discovery rate. Recently, a singular value decomposition (SVD) based approach was proposed for time series microarray data reduction, however it turned out not to be efficient for classifying microarray data. To overcome the shortcomings of these approaches, this paper proposes two methods to reduce false discovery rates. The first method involves an iterative t-test which finds the p-value for each gene under perturbation by eliminating one sample at a time. It eliminates weak noisy genes by dropping any gene which does not show significant p-value under all the conditions. The second method is a hybrid process which adapts a combination of the SVD and the t-test. It considers the entropy of all the data, and thus takes the correlation between genes into account. Classification accuracy is used to validate the significance of the extracted genes. The reported test results on two datasets demonstrate the applicability and effectiveness of the two proposed methods.

关键词： gene extraction gene reduction entropy microarray data t-test singular value decomposition p-value

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New clustering and feature selection procedures with applications to gene microarray data

New clustering and feature selection procedures with applica...

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作者： Xu, Yaomin Case Western Reserve University

学位级别：Ph.D.

Statistical data mining is one of the most active research areas. In this thesis we develop two new data mining procedures and explore their applications to genetic data. The first procedure is called PfCluster—Profile Cluster Analysis. It is a clustering method designed for profiled genetic data. The PfCluster is efficient and flexible in uncovering clusters determined by a new class of biologically meaningful distance metrics. A new internal quality measure of clusters, coherence index, is developed to find coherent clusters. An efficient mechanism for choosing the threshold of coherent clusters is also derived and implemented. The threshold is based on the first and second order approximations to the true threshold under a null distribution for parallel clusters. The PfCluster has been applied to simulated data and two real data examples: a biomarker LOH dataset and a microarray gene expression dataset. PfCluster is competitive to the correlation-based clustering procedures. The second procedure is called RPselection—Resampling based partitioning selection. It is a feature selection algorithm designed for microarray studies. It selects a subset of genes that maximizes a fitness score. The fitness score measures the relevance between the partition labels from a clustering result and an external class label derived from the clinical outcomes. The score is computed using a resampling procedure. The RPselection algorithm has been applied to simulated data and a real uveal melanoma gene expression data. RPselection outperforms gene-by-gene test-based feature selection procedures. Software development is an integral part of modern statistical research. Two software packages, pfclust and rpselect, are developed in this thesis based on our PfCluster method and RPselection algorithm. Packages pfclust and rpselect are implemented based on R object-oriented programming framework, and they can be easily customized and extended by users. The ideas in our two procedures can be

关键词： Statistics Bioinformatics coherence index data mining feature selection gene expression pathway gene profiling informative gene microarray data profile cluster analysis partitioning regulatory network statistical pattern recognition

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Analysis of Gene Network in MCF-7 Human Breast Cancer Cells

Analysis of Gene Network in MCF-7 Human Breast Cancer Cells

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12th International Conference on Control, Automation and Systems (ICCAS)

作者： Shiraishi, Ryohei Nakakuki, Takashi Kogakuin Univ Dept Mech Syst Engn Tokyo 1638677 Japan Kogakuin Univ Mech Engn Tokyo 1638677 Japan

ISBN: (纸本)9788993215045

Recent technological progress on high-throughput measurements for gene expression such as microarray analysis enables us to collect time-series gene expression data for each of tens of thousands of genes. Although a genomic analysis with those data has identified key genes relating to various diseases, few results on estimation of gene regulatory networks with real microarray data are available so far. Recently, the immediately early response (IER) genes upon epidermal growth factor stimulation in a human breast cancer cell line, MCF-7, have been identified in which time-course microarray data were measured during 90 minutes and 63 IER genes were chosen from tens of thousands of genes by using statistical analysis. In this paper, we estimate the gene regulatory networks among the 63 IER genes. To this end, we apply an estimation method based on a mixed logic dynamical modeling developed in an earlier study to the microarray data. However, the original method is executable for continuous gene expression time-series data whereas the real microarray time-course data have very few time points. In addition, some presetting parameters in the model are critical for a successful result on a network estimation. Then, we add a preprocessing and Monte Carlo-based calculation for the original method.

关键词： Gene network Network estimation Mixed logic dynamical modeling Constrained optimization problem microarray data

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Learning undirected graphical models from multiple datasets with the generalized non-rejection rate

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2012年第9期53卷 1326-1335页

作者： Roverato, Alberto Castelo, Robert Univ Bologna Bologna Italy Univ Pompeu Fabra Barcelona Spain

Learning graphical models from multiple datasets constitutes an appealing approach to learn transcriptional regulatory interactions from microarray data in the field of molecular biology. This has been approached both in a model based learning approach and in a model free learning approach where, in the latter, it is common practice to pool datasets produced under different experimental conditions. In this paper, we introduce a quantity called the generalized non-rejection rate which extends the non-rejection rate, introduced by Castelo and Roverato [3] so as to explicitly keep into account the different graphical models representing distinct experimental conditions involved in the structure of the dataset produced in multiple experimental batches. We show that the generalized non-rejection rate allows one to learn the common edges occurring throughout all graphical models, making it specially suited to identify robust transcriptional interactions which are common to all the considered experiments. The generalized non-rejection rate is then applied to both synthetic and real data and shown to provide competitive performance with respect to other widely used methods. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Graphical model microarray data Non-rejection rate Partial correlation Small-sample inference Multiple data sets

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Computational Identification of Proteins Sub-network in Parkinson's Disease Study

Computational Identification of Proteins Sub-network in Park...

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6th International Conference on Anti-Counterfeiting, Security and Identification (ASID)

作者： Huang, Yue Zhang, Jun Huang, Yunying Xiamen Univ Dept Commun Engn Xiamen Peoples R China Xiamen Univ Tan Kah Kee Coll Dept Elect Engn Xiamen Peoples R China

ISBN: (纸本)9781467321433

Parkinson's disease (PD) is a typical case of neurodegenerative disorder, which often impairs the sufferer's motor skills, speech, and other functions. Combination of protein-protein interaction (PPI) network analysis and gene expression studies provides a better insight of Parkinson's disease. In our work a computational approach was developed to identify protein signal network in PD study. First, a network-constrain regularization analysis is employed to the linear regression model for gene expression data from transgenic mouse models in normal and with Parkinson's disease. Proteins sub-network was then detected based on an integer linear programming model by integrating microarray data and PPI database.

关键词： Parkinson's disease microarray data linear regression model integer linear programming protein network detection

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Utilization of Attribute Clustering Methods for Scalable Computation of Reducts from High-Dimensional data

Utilization of Attribute Clustering Methods for Scalable Com...

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Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Janusz, Andrzej Slezak, Dominik Univ Warsaw Inst Math PL-02097 Warsaw Poland

ISBN: (纸本)9788360810484

We investigate methods for attribute clustering and their possible applications to a task of computation of decision reducts from information systems. We focus on high-dimensional data sets, for which the problem of selecting attributes that constitute a reduct can be extremely computationally intensive. We apply an attribute clustering method to facilitate construction of reducts from microarray data. Our experiments confirm that by proper grouping of similar, in some sense replaceable attributes it is possible to significantly decrease a computation time, as well as increase a quality of resulting reducts (i.e. decrease their average size).

关键词： attribute selection attribute reduction attribute clustering high-dimensional data microarray data scalable reducts computation methods

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Evolutionary Biclustering Algorithm of Gene Expression data

Evolutionary Biclustering Algorithm of Gene Expression Data

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23rd International Conference on database and Expert Systems Applications (DEXA)

作者： Ayadi, Wassim Maatouk, Ons Bouziri, Hend Univ Tunis ESSTT LaTICE Lab Tunis Tunisia Univ Tunis ISG Tunis LARODEC Lab Tunis Tunisia Univ Tunis ESSEC Tunis Tunis Tunisia

ISBN: (纸本)9780769548012

microarrays represent a new technology for measuring expression levels of several genes under various biological conditions generating multiple data. These data can be analyzed by using biclustering method which aims to extract a maximum number of genes and conditions presenting a similar behavior. This paper proposes a new evolutionary approach to obtain maximal high-quality biclusters of highly-correlated genes. The performance of the proposed algorithm is assessed on synthetic gene expression data. Experimental results show that our algorithm competes favorably with several state-of-the-art biclustering algorithms.

关键词： Biclustering Evolutionary algorithm microarray data

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