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4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE)

作者： Qiang Bo Wang Zheng-Zhi Natl Univ Def Technol Coll Mechatron Engn & Automat Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781424447138

The researches of module of co-regulated genes act as important role in research of gene regulatory networks. Present analysis of module of co-regulated genes generally have 2 problems: The first one is that the co-expressed genes are generally clustered as the co-regulated genes, but recent studies show that only 50% genes are co-regulated when their Spearman correlative coefficients are bigger than 0.84, so the co-regulated genes need to be refined from cluster of co-expressed genes;the other problem is that the common transcription factor (TF) is usually unknown, this makes obstacle in quantitative analysis on regulation relationships between TF and target genes. This paper presents a method for analysis of module about co-regulated genes by integrating information from sequences and microarray data. Firstly we cluster the genes by the similarity of expression profiles;then the co-regulated genes are refined by analysis of transcription factor binding sites (TFBS) in their promoter regions;the expression level of the gene corresponding to the common transcription factor can be estimated by Viterbi decoding based on Hidden Markov Model (HMM), whose parameters are learned by Baum-Welch algorithm. By consulting the related articles of experiments and standard database, the correctness and effectiveness of our method are validated.

关键词： Module of co-regulated genes Sequence of Gene microarray data Co-regulated Genes Transcription Factor Binding Sites

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data Integration Model for Cancer Subtype Identification using Kernel Dimensionality Reduction-Support Vector Machine (KDR-SVM)

Data Integration Model for Cancer Subtype Identification usi...

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7th International Conference on Computing and Convergence Technology (ICCCT)

作者： Wasito, Ito Istiqlal, Aulia N. Budi, Indra Univ Indonesia Fac Comp Sci Depok Indonesia

ISBN: (纸本)9781467308946;9788994364261

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-IO 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

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BASIS-EXPANSION FACTOR MODELS FOR UNCOVERING TRANSCRIPTION FACTOR REGULATORY NETWORK

BASIS-EXPANSION FACTOR MODELS FOR UNCOVERING TRANSCRIPTION F...

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IEEE Statistical Signal Processing Workshop (SSP)

作者： Sanchez-Castillo, M. Meng, Jia Tienda-Luna, I. M. Huang, Yufei Univ Granada Dept Appl Phys E-18071 Granada Spain Univ Texas San Antonio Dept Elect & Comp Sci San Antonio TX USA Univ Granada Dept Elect Engn & Comp Sci Granada Spain Univ Texas Hlth Sci Ctr San Antonio Dept Epidemiol & Biostat San Antonio TX 78229 USA

ISBN: (纸本)9781467301831

Uncovering transcription factor (TF) mediated regulatory networks from microarray expression data and prior knowledge is considered in this paper. Bayesian factor models that models direct TF regulation are formulated. To address the enormous computational complexity of the factor for modeling large networks, a novel, efficient basis-expansion factor (BE-FaM) model has been proposed, where the loading (regulatory) matrix is modeled as an expansion of basis functions of much lower dimension. Great reduction is achieved with BE-FaM as the inference involves instead estimation of expansion coefficients with much reduced dimensions. We also address the issue of incorporating the prior knowledge of TF regulation to constrain the factor loading matrix. A Gibbs sampling solution has been developed to estimate the unknowns. The proposed model was validated by the simulation and then applied to the genomic data of the breast cancer to uncover the corresponding TF regulatory networks.

关键词： Bayesian factor model Wavelet decomposition Sparse representation microarray data Breast cancer subtyping

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Detection of Differentially Expressed Genes in Wild Type HIV-1 Vpr and Two HIV-1 Mutant Vprs 3

Detection of Differentially Expressed Genes in Wild Type HIV...

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3rd International Conference on Frontiers in Intelligent Computing - Theory and Applications (FICTA)

作者： Barman, Bandana Mukhopadhyay, Anirban Kalyani Govt Engn Coll Dept Elect & Commun Engn Nadia W Bengal India Univ Kalyani Dept Comp Sci & Engn Nadia W Bengal India

ISBN: (纸本)9783319119328

Identification of differentially expressed genes between two sample groups are important to find which genes are increased in expression (up-regulated) or decreased in expression (down-regulated). We have identified differentially expressed genes between wild type HIV-1 Vpr and two HIV-1 mutant Vprs separately by using statistical t-test and false discovery rate. We also compute q-value of test to measure minimum FDR which occurs. We have found 1524 number of differentially expressed genes between wild type HIV-1 vpr and HIV-1 mutant vpr, R80A. Again we found 1525 diffrential genes between wild type HIV1 vpr and HIV-1 mutant vpr, F72A/R73A. From these two differentially expressed gene sets we get 941 number of down-regulated genes for both sets and rest genes are found as up-regulated genes.

关键词： microarray data p-value false discovery data q-value normalization permutation test differential gene expression

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Relevant attribute discovery in high dimensional data:: Application to breast cancer gene expressions

Relevant attribute discovery in high dimensional data:: Appl...

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1st International Conference on Rough Sets and Knowledge Technology (RSKT 2006)

作者： Valdes, Julio J. Barton, Alan J. Natl Res Council Canada Ottawa ON K1A 0R6 Canada

ISBN: (纸本)3540362975

In many domains, the data objects are described in terms of a large number of features. The pipelined data mining approach introduced in [1] using two clustering algorithms in combination with rough sets and extended with genetic programming, is investigated with the purpose of discovering important subsets of attributes in high dimensional data. Their classification ability is described in terms of both collections of rules and analytic functions obtained by genetic programming (gene expression programming). The Leader and several k-means algorithms are used as procedures for attribute set simplification of the information systems later presented to rough sets algorithms. Visual data raining techniques including virtual reality were used for inspecting results. The data mining process is setup using high throughput distributed computing techniques. This approach was applied to Breast Cancer microarray data and it led to subsets of genes with high discrimination power with respect to the decision classes.

关键词： clustering rough sets reducts rules cross-validation gene expression programming virtual reality grid computing breast cancer microarray data

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Two Extensions to Multi-label Correlation-Based Feature Selection: a case study in bioinformatics

Two Extensions to Multi-label Correlation-Based Feature Sele...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Jungjit, Suwimol Freitas, Alex A. Michaelis, M. Cinatl, J. Univ Kent Sch Comp Canterbury CT2 7NF Kent England

ISBN: (纸本)9781479906529

This paper proposes two extensions to a Multi-Label Correlation Based Feature Selection Method (ML-CFS): (1) ML-CFS using the absolute value of the correlation coefficient in the equation for evaluating a candidate feature subset, and (2) ML-CFS using Mutual Information for class label weighting. These extensions are evaluated in a bioinformatics case study addressing the multi-label classification of a cancer-related DNA microarray dataset with over 20,000 features. The results show that ML-CFS with absolute value of correlation obtained a significantly better predictive accuracy (smaller hamming loss) than the original ML-CFS. On the other hand, using Mutual Information to assign weights to labels showed some positive effect when using the ML-RBF classifier, but it showed a negative effect when using the ML-kNN classifier.

关键词： multi-label feature selection multi-label classification microarray data

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Dynamic models of gene expression and classification

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Functional and Integrative Genomics 2001年第4期1卷 269-278页

作者： Dewey, T.G. Galas, D.J. Keck Grad. Inst. Applied Life Sci. Claremont CA 91711 535 Watson Drive United States

Powerful new methods, like expression profiles using cDNA arrays, have been used to monitor changes in gene expression levels as a result of a variety of metabolic, xenobiotic or pathogenic challenges. This potentially vast quantity of data enables, in principle, the dissection of the complex genetic networks that control the patterns and rhythms of gene expression in the cell. Here we present a general approach to developing dynamic models for analyzing time series of whole genome expression. In this approach, a self-consistent calculation is performed that involves both linear and non-linear response terms for interrelating gene expression levels. This calculation uses singular value decomposition (SVD) not as a statistical tool but as a means of inverting noisy and near-singular matrices. The linear transition matrix that is determined from this calculation can be used to calculate the underlying network reflected in the data. This suggests a direct method of classifying genes according to their place in the resulting network. In addition to providing a means to model such a large multi-variate system this approach can be used to reduce the dimensionality of the problem in a rational and consistent way, and suppress the strong noise amplification effects often encountered with expression profile data. Non-linear and higher-order Markov behavior of the network are also determined in this self-consistent method. In data sets from yeast, we calculate the Markov matrix and the gene classes based on the linear-Markov network. These results compare favorably with previously used methods like cluster analysis. Our dynamic method appears to give a broad and general framework for data analysis and modeling of gene expression arrays.

关键词： Gene control Gene expression profiles microarray data Time-series analysis

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Testing differential expression in nonoverlapping gene pairs: A new perspective for the empirical bayes method

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Journal of Bioinformatics and Computational Biology 2008年第2期6卷 301-316页

作者： Klebanov, Lev Qiu, Xing Yakovlev, Andrei Department of Probability and Statistics Charles University Praha-8 CZ-18675 Sokolovska 83 Czech Republic Department of Biostatistics and Computational Biology University of Rochester Rochester NY 14642 601 Elmwood Avenue United States

The currently practiced methods of significance testing in microarray gene expression profiling are highly unstable and tend to be very low in power. These undesirable properties are due to the nature of multiple testing procedures, as well as extremely strong and long-ranged correlations between gene expression levels. In an earlier publication, we identified a special structure in gene expression data that produces a sequence of weakly dependent random variables. This structure, termed the δ-sequence, lies at the heart of a new methodology for selecting differentially expressed genes in nonoverlapping gene pairs. The proposed method has two distinct advantages: (1) it leads to dramatic gains in terms of the mean numbers of true and false discoveries, and in the stability of the results of testing;and (2) its outcomes are entirely free from the log-additive array-specific technical noise. We demonstrate the usefulness of this approach in conjunction with the nonparametric empirical Bayes method. The proposed modification of the empirical Bayes method leads to significant improvements in its performance. The new paradigm arising from the existence of the δ-sequence in biological data offers considerable scope for future developments in this area of methodological research. © 2008 Imperial College Press.

关键词： Correlation Differential expression Error rates Gene pairs microarray data

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Genetic data Classification Based on Improved Rank Aggregation 3

Genetic Data Classification Based on Improved Rank Aggregati...

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3rd International Conference on Systems and Informatics (ICSAI)

作者： Jiang, Linying Shi, Xin Yu, Donghai Northeastern Univ Software Coll Shenyang Peoples R China

ISBN: (纸本)9781509055210

The number of genes in microarray data is much larger than the number of available effective samples. Therefore, dealing with a small number of microarray data which are represented by high dimensional features, but of high correlations and strong interferences of redundancy and noise, has become one of the important tasks in gene microarray data extraction and classification. In this paper, a new method is proposed. We use wavelet decomposition to extract gene microarray data, and then use the t-test, ReliefF, Wilcoxon test, and other algorithms to select the data after wavelet transform. Finally, the Borda method is used to merge the sorted values. Three datasets were used in the experiment, namely the leukemia dataset, the prostate dataset, and the lung cancer dataset. Experimental results show that the method proposed in this paper can effectively classify the cancer gene microarray data.

关键词： component microarray data wavelet decomposition feature extraction feature selection rank aggregation

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Identification of Differentially Expressed Genes through a Meta-analysis Approach for Oral Cancer Classification 39

Identification of Differentially Expressed Genes through a M...

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39th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Kourou, Konstantina Papaloukas, Costas Fotiadis, Dimitrios I. Univ Ioannina Dept Mat Sci & Engn Unit Med Technol & Intelligent Informat Syst GR-45110 Ioannina Greece Fdn Res & Technol Hellas FORTH Iraklion Greece Univ Ioannina Dept Biol Applicat & Technol GR-45110 Ioannina Greece

ISBN: (纸本)9781509028092

We propose a meta-analysis scheme for identifying differentially expressed genes in Oral Squamous Cell Carcinoma (OSCC) from different microarray studies. We detect a subset of relevant features and further classify samples under two experimental conditions (i.e healthy and cancer samples) for better patient stratification. A well-established meta-analysis method is adopted and gene expression data sets are derived from a public functional genomics data repository. Our primary aim is the accurate identification of up-and down-regulated genes in order to extract valuable biological information concerning the changes in expression between healthy and cancer samples. According to our results and the extracted informative gene list, a high classification accuracy of healthy and OSCC tumors is achieved with as few genes as possible. Furthermore, the proposed scheme implies that the combination of datasets from different origins may reduce the estimated percentage of false predictions, while the power of gene identification and disease classification is increased.

关键词： differential expression disease classification informative genes meta-analysis microarray data oral cancer

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