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IEEE International Conference on Systems, Man and Cybernetics

作者： Meng-Hsiun Tsai Ching-Flao Lai Shin-Jr Lu Shun-Feng Su Natl Chung Hsing Univ Dept Management Informat Syst Taichung Taiwan Natl Chung Hsing Univ Inst Comp Sci Taichung Taiwan Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei Taiwan

ISBN: (纸本)9781424400997

In this paper we present some performance comparisons of several unsupervised clustering techniques include: Self-Organizing Map (SOM), Fuzzy C-means (FCM) and hierarchical clustering, and they are employed to analyze the ovarian cancer microarray data. The data includes 15 samples with 9,600 genes and these samples include 5 benign ovarian tumors (OVT), I borderline ovarian malignancy (OVTT), 4 ovarian cancers at stage I (OVCAI), and 5 ovarian cancers at stage III (OVCAIII). A regression analysis is used to reduce the dimension and get 9600 residuals of genes. The genes with 100 largest and 100 smallest residual are picked to analyze using analysis of variance (ANOVA). After the ANOVA, 12 gene markers are got and can be used to distinguish OVT, OVTT, OVCAI and OVCAIII samples. The 12 gene markers are performed clustering by the SOM, FCM and hierarchical clustering techniques and to compare the results between these clustering techniques. Our experimental results show that the hierarchical clustering can get best performance of clustering and users do not need to define the number of clusters.

关键词： Analysis of variance Clustering Fuzzy c-means Gene marker Hierarchical clustering microarray data Ovarian cancer Self-organizing map

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A robust procedure for Gaussian graphical model search from microarray data with p larger than n

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JOURNAL OF MACHINE LEARNING RESEARCH 2006年 7卷 2621-2650页

作者： Castelo, Robert Roverato, Alberto Univ Pompeu Fabra Dept Ciencies Expt & Salut E-08003 Barcelona Spain Univ Bologna Dipartimento Sci Stat I-40126 Bologna Italy

Learning of large-scale networks of interactions from microarray data is an important and challenging problem in bioinformatics. A widely used approach is to assume that the available data constitute a random sample from a multivariate distribution belonging to a Gaussian graphical model. As a consequence, the prime objects of inference are full-order partial correlations which are partial correlations between two variables given the remaining ones. In the context of microarray data the number of variables exceed the sample size and this precludes the application of traditional structure learning procedures because a sampling version of full-order partial correlations does not exist. In this paper we consider limited-order partial correlations, these are partial correlations computed on marginal distributions of manageable size, and provide a set of rules that allow one to assess the usefulness of these quantities to derive the independence structure of the underlying Gaussian graphical model. Furthermore, we introduce a novel structure learning procedure based on a quantity, obtained from limited-order partial correlations, that we call the non-rejection rate. The applicability and usefulness of the procedure are demonstrated by both simulated and real data.

关键词： Gaussian distribution gene network graphical model microarray data non-rejection rate partial correlation small-sample inference

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A comparative study of classification methods for microarray data analysis 06

A comparative study of classification methods for microarray...

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Proceedings of the fifth Australasian conference on data mining and analystics - Volume 61

作者： Hong Hu Jiuyong Li Ashley Plank Hua Wang Grant Daggard University of Southern Queensland Toowoomba QLD Australia

ISBN: (纸本)9781920682415

In response to the rapid development of DNA microarray technology, many classification methods have been used for microarray classification. SVMs, decision trees, Bagging, Boosting and Random Forest are commonly used methods. In this paper, we conduct experimental comparison of LibSVMs, C4.5, BaggingC4.5, AdaBoostingC4.5, and Random Forest on seven microarray cancer data sets. The experimental results show that all ensemble methods outperform C4.5. The experimental results also show that all five methods benefit from data preprocessing, including gene selection and discretization, in classification accuracy. In addition to comparing the average accuracies of ten-fold cross validation tests on seven data sets, we use two statistical tests to validate findings. We observe that Wilcoxon signed rank test is better than sign test for such purpose.

关键词： microarray data classification

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An efficient gene selection algorithm based on mutual information

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NEUROCOMPUTING 2009年第4-6期72卷 991-999页

作者： Cai, Ruichu Hao, Zhifeng Yang, Xiaowei Wen, Wen S China Univ Technol Sch Engn & Comp Sci Guangzhou 510640 Guangdong Peoples R China S China Univ Technol Coll Math Sci Guangzhou 510640 Guangdong Peoples R China

Gene selection, a significant preprocessing of the discriminant analysis of microarray data, is to select the most informative genes from the whole gene set. In this paper, an efficient mutual information-based gene selection algorithm (MIGS) is proposed, in which genes are sequentially forward selected according to an approximate measure of the mutual information between the class and the selected genes. In order to improve the efficiency of the MIGS, an effective pruning strategy is introduced in the selection procedure as well as the employment of Parzen window density estimation technique. Extensive experiments are conducted on three public gene expression datasets and the experimental results confirm the efficiency and effectiveness of the algorithm, Though the computational cost of MIGS-Pruning increases with the number of selected genes, it still has good performance applied in the microarray problems. (C) 2008 Elsevier B.V. All rights reserved.

关键词： microarray data Gene expression Gene selection Mutual information Parzen window density estimation

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A maximally diversified multiple decision tree algorithm for microarray data classification 06

A maximally diversified multiple decision tree algorithm for...

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Proceedings of the 2006 workshop on Intelligent systems for bioinformatics - Volume 73

作者： Hong Hu Jiuyong Li Hua Wang Grant Daggard Mingren Shi University of Southern Queensland Toowoomba QLD Australia

ISBN: (纸本)9781920682545

We investigate the idea of using diversified multiple trees for microarray data classification. We propose an algorithm of Maximally Diversified Multiple Trees (MDMT), which makes use of a set of unique trees in the decision committee. We compare MDMT with some well-known ensemble methods, namely AdaBoost, Bagging, and Random Forests. We also compare MDMT with a diversified decision tree algorithm, Cascading and Sharing trees (CS4), which forms the decision committee by using a set of trees with distinct roots. Based on seven microarray data sets, both MDMT and CS4 are more accurate on average than AdaBoost, Bagging, and Random Forests. Based on a sign test of 95% confidence, both MDMT and CS4 perform better than majority traditional ensemble methods tested. We discuss differences between MDMT and CS4.

关键词： decision tree microarray data diversified classifiers ensemble classifier

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EDGE³: A web-based solution for management and analysis of Agilent two color microarray experiments

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BMC BIOINFORMATICS 2009年第1期10卷 1-10页

作者： Vollrath, Aaron L. Smith, Adam A. Craven, Mark Bradfield, Christopher A. Univ Wisconsin McArdle Lab Canc Res Sch Med & Publ Hlth Madison WI 53706 USA Univ Wisconsin Dept Biostat & Med Informat Madison WI 53706 USA Univ Wisconsin Dept Comp Sci Madison WI 53706 USA

Background: The ability to generate transcriptional data on the scale of entire genomes has been a boon both in the improvement of biological understanding and in the amount of data generated. The latter, the amount of data generated, has implications when it comes to effective storage, analysis and sharing of these data. A number of software tools have been developed to store, analyze, and share microarray data. However, a majority of these tools do not offer all of these features nor do they specifically target the commonly used two color Agilent DNA microarray platform. Thus, the motivating factor for the development of EDGE(3) was to incorporate the storage, analysis and sharing of microarray data in a manner that would provide a means for research groups to collaborate on Agilent-based microarray experiments without a large investment in software-related expenditures or extensive training of end-users. Results: EDGE(3) has been developed with two major functions in mind. The first function is to provide a workflow process for the generation of microarray data by a research laboratory or a microarray facility. The second is to store, analyze, and share microarray data in a manner that doesn't require complicated software. To satisfy the first function, EDGE(3) has been developed as a means to establish a well defined experimental workflow and information system for microarray generation. To satisfy the second function, the software application utilized as the user interface of EDGE(3) is a web browser. Within the web browser, a user is able to access the entire functionality, including, but not limited to, the ability to perform a number of bioinformatics based analyses, collaborate between research groups through a user-based security model, and access to the raw data files and quality control files generated by the software used to extract the signals from an array image. Conclusion: Here, we present EDGE(3), an open-source, web-based application that allows f

关键词： microarray data Query Object Agilent Feature Extraction Software data Analysis Section Array Object

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Gene interaction - An evolutionary biclustering approach

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INFORMATION FUSION 2009年第3期10卷 242-249页

作者： Mitra, Sushmita Das, Ranajit Banka, Haider Mukhopadhyay, Subhasis Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India Indian Stat Inst Ctr Soft Comp Res Kolkata 700108 India Univ Calcutta Dept Biophys Mol Biol & Genet Bioinformat Ctr Kolkata 700009 India

DNA microarray experiments form a powerful tool for studying gene expression patterns, in large scale. Sharing of the regulatory mechanism among genes, in an organism, is predominantly responsible for their co-expression. Biclustering aims at finding a subset of similarly expressed genes under a subset of experimental conditions. A small number of genes participate in a cellular process of interest. Again, a gene may be simultaneously involved in a number of cellular processes. In cellular environment, genes interact among themselves to produce enzymes, metabolites, proteins, etc. responsible for a particular function(s). In this study, a simple and novel correlation-based approach is proposed to extract gene interaction networks from biclusters in microarray data. Local search strategy is employed to add (remove) relevant (irrelevant) genes for finer tuning, in multi-objective biclustering framework. Preprocessing is done to preserve strongly correlated gene interaction pairs. Experimental results on time-series gene expression data from Yeast are biologically validated using benchmark databases and literature. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Biclustering Transcriptional regulatory network extraction Bioinformatics microarray data Gene expression profile Gene interaction network

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Robust data imputation

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2009年第1期33卷 7-13页

作者： Branden, Karlien Vanden Verboven, Sabine Univ Antwerp Dept Math Stat & Actuarial Sci B-2000 Antwerp Belgium Commiss European Communities Joint Res Ctr I-21020 Ispra Italy

Single imputation methods have been wide-discussed topics among researchers in the field of bioinformatics. One major shortcoming of methods proposed until now is the lack of robustness considerations. Like all data, gene expression data can possess outlying values. The presence of these outliers could have negative effects on the imputated values for the missing values. Afterwards, the outcome of any statistical analysis on the completed data could lead to incorrect conclusions. Therefore it is important to consider the possibility of outliers in the data set, and to evaluate how imputation techniques will handle these values. In this paper, a simulation study is performed to test existing techniques for data imputation in case outlying values are present in the *** some shortcomings of the existing imputation techniques, a new robust imputation method that can deal with the presence of outliers in the data is introduced. In addition, the robust imputation procedure cleans the data for further statistical analysis. Moreover, this method can be easily extended towards a Multiple imputation approach by which the uncertainty of the imputed values is emphasised. Finally, a classification example illustrates the lack of robustness of some existing imputation methods and shows the advantage of the multiple imputation approach of the new robust imputation technique. (c) 2008 Elsevier Ltd. All rights reserved.

关键词： Missing genes microarray data Imputation methods Robust statistics

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Use of Bayesian networks to probabilistically model and improve the likelihood of validation of microarray findings by RT-PCR

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JOURNAL OF BIOMEDICAL INFORMATICS 2009年第2期42卷 287-295页

作者： English, Sangeeta B. Shih, Shou-Ching Ramoni, Marco F. Smith, Lois E. Butte, Atul J. Stanford Univ Sch Med Stanford Ctr Biomed Informat Res BMIR Stanford CA 94305 USA Beth Israel Deaconess Med Ctr Res N Dept Pathol Boston MA 02215 USA Childrens Hosp Boston Dept Ophthalmol Boston MA 02115 USA Harvard Univ Sch Med Harvard Partners Ctr Genet & Genom Boston MA 02115 USA Harvard Univ Sch Med Harvard MIT Div Hlth Sci & Technol Childrens Hosp Informat Program Boston MA 02115 USA

Though genome-wide technologies, such as microarrays, are widely used, data from these methods are considered noisy: there is still varied Success in downstream biological validation. We report a method that increases the likelihood Of Successfully validating microarray findings using real time RT-PCR. including genes at low expression levels and with small differences. We use a Bayesian network to identify the most relevant sources of noise based on the successes and failures in validation for an initial set of selected genes, and then improve Our subsequent selection of genes for validation based on eliminating these sources of noise. The network displays the significant Sources of noise in an experiment, and scores the likelihood of validation for every gene. We show how the method can significantly increase validation success rates. In conclusion, in this study, we have Successfully added a new automated step to determine the contributory sources of noise that determine successful or unsuccessful downstream biological validation. (C) 2008 Elsevier Inc. All rights reserved.

关键词： Bioinformatics Bayesian network microarray RT-PCR microarray data

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In Silico Evaluation of Predicted Regulatory Interactions in Arabidopsis thaliana

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BMC BIOINFORMATICS 2009年第1期10卷 1-15页

作者： Nero, Damion Katari, Manpreet S. Kelfer, Jonathan Tranchina, Daniel Coruzzi, Gloria M. NYU Dept Biol Ctr Genom & Syst Biol New York NY 10003 USA NYU Courant Inst Math Sci New York NY 10003 USA

Background: Prediction of transcriptional regulatory mechanisms in Arabidopsis has become increasingly critical with the explosion of genomic data now available for both gene expression and gene sequence composition. We have shown in previous work [1], that a combination of correlation measurements and cis-regulatory element (CRE) detection methods are effective in predicting targets for candidate transcription factors for specific case studies which were validated. However, to date there has been no quantitative assessment as to which correlation measures or CRE detection methods used alone or in combination are most effective in predicting TF -> target relationships on a genome-wide scale. Results: We tested several widely used methods, based on correlation (Pearson and Spearman Rank correlation) and cis-regulatory element (CRE) detection (>= 1 CRE or CRE over-representation), to determine which of these methods individually or in combination is the most effective by various measures for making regulatory predictions. To predict the regulatory targets of a transcription factor ( TF) of interest, we applied these methods to microarray expression data for genes that were regulated over treatment and control conditions in wild type (WT) plants. Because the chosen data sets included identical experimental conditions used on TF over-expressor or T-DNA knockout plants, we were able to test the TF. target predictions made using microarray data from WT plants, with microarray data from mutant/transgenic plants. For each method, or combination of methods, we computed sensitivity, specificity, positive and negative predictive value and the F-measure of balance between sensitivity and positive predictive value (precision). This analysis revealed that the >= 1 CRE and Spearman correlation (used alone or in combination) were the most balanced CRE detection and correlation methods, respectively with regard to their power to accurately predict regulatory-target interactions. Con

关键词： microarray data Positive Predictor Value Spearman Correlation microarray dataset Regulatory Prediction

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