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International Conference on Computer Sciences and Convergence Information Technology (ICCIT)

作者： Ito Wasito Aulia N. Istiqlal Indra Budi Faculty of Computer Science Universitas Indonesia Depok Indonesia

ISBN: (纸本)9781467308946

In this paper, an integration model of cancer patients data types such as microarray DNA and clinical data will be experimentally explored. The data of integration will be used for cancer subtype identification using kernel based classification methods which is the extension of Support Vector Machine (SVM) approach with Kernel Dimensionality Reduction (KDR). KDR-SVM method will be implemented in Lymphoma cancer database and the relevant clinical information. data type representation will be modeled in an appropriate kernel matrix. The results of the experiment show that the KDR-10 dimensions and data integration can improve the accuracy of the identification of subtype cancer.

关键词： data Integration microarray data Clinical data Kernel Dimensionality Reduction Support Vector Machine Kernel Matrix data integration Support Vector Network Seeds Cancer Cancer data types vector computer Kernel Classification Methods

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A Integrated Computational Approach for Protein Sub-network Detection in Parkinson's Disease

A Integrated Computational Approach for Protein Sub-network ...

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2010 3rd International Conference on Computer and Electrical Engineering(ICCEE 2010)

作者： Yue Huang Yunying Huang Institute of Signal and Information Processing Department of Communication Engineering Xiamen University Department of Electronic Engineering Xiamen University

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. A computational approach was developed in our work to identify protein signal network in PD study. First, a linear regression model is setup and then a network-constrain regularization analysis was applied to microarray data from transgenic mouse model with Parkinson's disease. Then protein network was 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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A generalized false discovery rate in microarray studies

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COMPUTATIONAL STATISTICS & data ANALYSIS 2011年第1期55卷 731-737页

作者： Kang, Moonsu Chun, Heuiju Hanyang Univ Coll Med Dept Physiol Seoul 133791 South Korea Pusan Univ Foreign Studies Coll Commerce & Business Dept Data Management Pusan 608347 South Korea

The problem of identifying differentially expressed genes is considered in a microarray experiment. This motivates us to involve an appropriate multiple testing setup to high dimensional and low sample size testing problems in highly nonstandard setups. Family-wise error rate (FWER) is too conservative to control the type I error, whereas a less conservative false discovery rate has received considerable attention in a wide variety of research areas such as genomics and large biological systems. Recently, a less conservative method than FDR, the k-FDR, which generalizes the FDR has been proposed by Sarkar (2007). Most of the current FDR procedures assume restrictive dependence structures, resulting in being less reliable. The purpose of this paper is to address these very large multiplicity problems by adopting a proposed k-FDR controlling procedure under suitable dependence structures and based on a Poisson distributional approximation in a unified framework. We compare the performance of the proposed k-FDR procedure with that of other FDR controlling procedures, with an illustration of the leukemia microarray study of Golub et al. (1999) and simulated data. For power consideration, different FOR procedures are assessed using false negative rate (FNR). An unbiased property is appraised by FOR <= alpha and a higher value of 1 - (FDR + FNR). The proposed k-FDR procedure is characterized by greater power without much elevation of k-FDR. (C) 2010 Elsevier B.V. All rights reserved.

关键词： microarray data Familywise error rate k-FDR Poisson approximation

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An integrated genetics approach for identifying protein signal pathways of Alzheimer's disease

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2011年第4期14卷 371-378页

作者： Huang, Yue Sun, Xuezhi Hu, Guangshu Tsinghua Univ Sch Med Dept Biomed Engn Beijing 100084 Peoples R China Natl Inst Radiol Sci Chiba 2658555 Japan

Alzheimer's disease (AD) is considered one of the most common age-associated neurodegenerative disorders, affecting millions of senior people worldwide. Combination of protein-protein interaction (PPI) network analysis and gene expression studies provides a better insight into AD. A computational approach was developed in our work to identify protein signal pathways between amyloid precursor proteins and tau proteins, which are well known as important proteins for AD. First, a modified LA-SEN method, called the network-constrained regularisation analysis, was applied to microarray data from a transgenic mouse model and AD patients. Then protein pathways were constructed based on an integer linear programming model to integrate microarray data and the PPI database. Important pathways of AD, including some cancer-related pathways, were identified finally.

关键词： Alzheimer's disease microarray data linear regression model LA-SEN linear programming model protein-protein interaction network

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A Weighted Principal Component Analysis and Its Application to Gene Expression data

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2011年第1期8卷 246-252页

作者： da Costa, Joaquim F. Pinto Alonso, Hugo Roque, Luis Univ Porto Fac Ciencias Dept Matemat P-4169007 Oporto Portugal Univ Porto CMUP Ctr Matemat Oporto Portugal Univ Lusofona Porto Fac Econ & Gestao P-4000098 Oporto Portugal Univ Aveiro Dept Matemat P-3810193 Aveiro Portugal Univ Aveiro CIDMA Aveiro Portugal Inst Super Engn Porto Grp Invest Engn Conhecimento & Apoio Decisao GECA P-4200072 Oporto Portugal

In this work, we introduce in the first part new developments in Principal Component Analysis (PCA) and in the second part a new method to select variables (genes in our application). Our focus is on problems where the values taken by each variable do not all have the same importance and where the data may be contaminated with noise and contain outliers, as is the case with microarray data. The usual PCA is not appropriate to deal with this kind of problems. In this context, we propose the use of a new correlation coefficient as an alternative to Pearson's. This leads to a so-called weighted PCA (WPCA). In order to illustrate the features of our WPCA and compare it with the usual PCA, we consider the problem of analyzing gene expression data sets. In the second part of this work, we propose a new PCA-based algorithm to iteratively select the most important genes in a microarray data set. We show that this algorithm produces better results when our WPCA is used instead of the usual PCA. Furthermore, by using Support Vector Machines, we show that it can compete with the Significance Analysis of microarrays algorithm.

关键词： Correlation principal component analysis support vector machines microarray data gene selection

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Regression models, scan statistics and reappearance probabilities to detect regions of association between gene expression and copy number

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STATISTICS IN MEDICINE 2011年第10期30卷 1157-1178页

作者： Asimit, Jennifer L. Andrulis, Irene L. Bull, Shelley B. Univ Toronto Mt Sinai Hosp Samuel Lunenfeld Res Inst Prosserman Ctr Hlth Res Toronto ON M5T 3L9 Canada Univ Toronto Dalla Lana Sch Publ Hlth Toronto ON M5T 3L9 Canada

Early studies of breast cancer microarray data used linear models to quantify the relationship between measures of gene expression (GE) and copy number (CN) obtained from tumour samples. Motivated by a study of women with axillary node-negative breast cancer, we propose a regression-based scan statistic to identify within-chromosome clusters of genetic probes that exhibit association between GE and CN, while accounting for tumour characteristics known to be prognostic for clinical outcome. As a measure of the association between GE and CN, for each genetic probe available from a microarray we regress GE on CN, and include subject-specific covariates. In the development of the scan statistic, the within-chromosome spatial distribution of the subset of probes with a statistically significant association is approximated by a Poisson process. By incorporating the distance between the probe positions, the scan statistic accounts for the spatial nature of CN alterations. Regions identified as clusters of significant associations are hypothesized to harbour genes involved in breast cancer progression. Using simulations, we examine the sensitivity of the method to certain factors, and to address issues of repeatability, we consider reappearance probabilities for each probe within detected regions and assess the utility of a quantity estimated by bootstrap sample frequencies. Applications of the proposed method to joint analysis of GE and CN in breast tumours, with and without an informative covariate, and comparisons with alternative methods suggest that inclusion of covariates and the use of a regional test statistic can serve to refine regions for further investigation including the analysis of their association with outcome. Copyright (C) 2011 John Wiley & Sons, Ltd.

关键词： bootstrap breast cancer microarray data repeatability subject-specific covariate

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High-throughput processing and normalization of one-color microarrays for transcriptional meta-analyses

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BMC BIOINFORMATICS 2011年第10-sup期12卷 S2-S2页

作者： Dozmorov, Mikhail G. Wren, Jonathan D. Oklahoma Med Res Fdn Arthrit & Clin Immunol Res Program Oklahoma City OK 73104 USA

Background: microarray experiments are becoming increasingly common in biomedical research, as is their deposition in publicly accessible repositories, such as Gene Expression Omnibus (GEO). As such, there has been a surge in interest to use this microarray data for meta-analytic approaches, whether to increase sample size for a more powerful analysis of a specific disease (e. g. lung cancer) or to re-examine experiments for reasons different than those examined in the initial, publishing study that generated them. For the average biomedical researcher, there are a number of practical barriers to conducting such meta-analyses such as manually aggregating, filtering and formatting the data. Methods to automatically process large repositories of microarray data into a standardized, directly comparable format will enable easier and more reliable access to microarray data to conduct meta-analyses. Methods: We present a straightforward, simple but robust against potential outliers method for automatic quality control and pre-processing of tens of thousands of single-channel microarray data files. GEO GDS files are quality checked by comparing parametric distributions and quantile normalized to enable direct comparison of expression level for subsequent meta-analyses. Results: 13,000 human 1-color experiments were processed to create a single gene expression matrix that subsets can be extracted from to conduct meta-analyses. Interestingly, we found that when conducting a global meta-analysis of gene-gene co-expression patterns across all 13,000 experiments to predict gene function, normalization had minimal improvement over using the raw data. Conclusions: Normalization of microarray data appears to be of minimal importance on analyses based on co-expression patterns when the sample size is on the order of thousands microarray datasets. Smaller subsets, however, are more prone to aberrations and artefacts, and effective means of automating normalization procedures not onl

关键词： microarray data Gene Expression Omnibus Quantile Normalization Affymetrix Human Genome Gene Expression Matrix

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Influence of Prior Knowledge in Constraint-Based Learning of Gene Regulatory Networks

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2011年第1期8卷 130-142页

作者： Tan, Mehmet Alshalalfa, Mohammed Alhajj, Reda Polat, Faruk TOBB Univ Econ & Technol Dept Comp Engn Ankara Turkey Univ Calgary Dept Comp Sci Calgary AB T2N 1N4 Canada Global Univ Dept Comp Sci Beirut Lebanon Middle E Tech Univ Dept Comp Engn TR-06531 Ankara Turkey

Constraint-based structure learning algorithms generally perform well on sparse graphs. Although sparsity is not uncommon, there are some domains where the underlying graph can have some dense regions;one of these domains is gene regulatory networks, which is the main motivation to undertake the study described in this paper. We propose a new constraint-based algorithm that can both increase the quality of output and decrease the computational requirements for learning the structure of gene regulatory networks. The algorithm is based on and extends the PC algorithm. Two different types of information are derived from the prior knowledge;one is the probability of existence of edges, and the other is the nodes that seem to be dependent on a large number of nodes compared to other nodes in the graph. Also a new method based on Gene Ontology for gene regulatory network validation is proposed. We demonstrate the applicability and effectiveness of the proposed algorithms on both synthetic and real data sets.

关键词： Gene regulatory networks transcription factors genes microarray data gene ontology prior knowledge-based learning

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Using the bagging approach for biclustering of gene expression data

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NEUROCOMPUTING 2011年第10期74卷 1595-1605页

作者： Hanczar, B. Nadif, M. Univ Paris 05 LIPADE F-75006 Paris France

Several methods have been proposed for microarray data analysis that enables to identify groups of genes with similar expression profiles only under a subset of examples. We propose to improve the performance of these biclustering methods by adapting the approach of bagging to biclustering problems. The principle consists in generating a set of biclusters and aggregating the results. Our method has been tested with success on both synthetic and real datasets. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Biclustering Bagging Ensemble method microarray data

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Gene selection and classification using Taguchi chaotic binary particle swarm optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第10期38卷 13367-13377页

作者： Chuang, Li-Yeh Yang, Cheng-San Wu, Kuo-Chuan Yang, Cheng-Hong Chia Yi Christian Hosp Dept Plast Surg Chiayi 60002 Taiwan I Shou Univ Inst Biotechnol & Chem Engn Kaohsiung 80041 Taiwan Natl Kaohsiung Univ Appl Sci Dept Comp Sci & Informat Engn Kaohsiung 80708 Taiwan Toko Univ Dept Network Syst Chiayi 61363 Taiwan Natl Kaohsiung Univ Appl Sci Dept Elect Engn Kaohsiung 80708 Taiwan

The purpose of gene expression analysis is to discriminate between classes of samples, and to predict the relative importance of each gene for sample classification. microarray data with reference to gene expression profiles have provided some valuable results related to a variety of problems and contributed to advances in clinical medicine. microarray data characteristically have a high dimension and a small sample size. This makes it difficult for a general classification method to obtain correct data for classification. However, not every gene is potentially relevant for distinguishing the sample class. Thus, in order to analyze gene expression profiles correctly, feature (gene) selection is crucial for the classification process, and an effective gene extraction method is necessary for eliminating irrelevant genes and decreasing the classification error rate. In this paper, correlation-based feature selection (CFS) and the Taguchi chaotic binary particle swarm optimization (TCBPSO) were combined into a hybrid method. The K-nearest neighbor (K-NN) with leave-one-out cross-validation (LOOCV) method served as a classifier for ten gene expression profiles. Experimental results show that this hybrid method effectively simplifies features selection by reducing the number of features needed. The classification error rate obtained by the proposed method had the lowest classification error rate for all of the ten gene expression data set problems tested. For six of the gene expression profile data sets a classification error rate of zero could be reached. The introduced method outperformed five other methods from the literature in terms of classification error rate. It could thus constitute a valuable tool for gene expression analysis in future studies. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： microarray data Correlation-based feature selection Taguchi-binary particle swarm optimization K-nearest neighbor

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