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

作者： Banka, Haider Dara, Suresh Indian Sch Mines Dept Comp Sci & Engn Dhanbad 826004 Bihar India

ISBN: (纸本)9780769548012

An evolutionary rough feature selection algorithm is proposed for classifying gene expression patterns. Since the data typically consist of a large number of redundant features, an initial redundancy reduction of the attributes is done to enable faster convergence. Rough set theory is employed to generate the distinction table that enable PSO to find reducts, which represent the minimal sets of non-redundant features capable of discerning between all objects. The effectiveness of the algorithm is demonstrated on three benchmark cancer datasets viz. Colon, Lymphoma and Leukemia using MOGA.

关键词： Soft computing bioinformatics microarray data feature selection reduct generation classification

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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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A NON-PARAMETRIC BAYESIAN CLUSTERING FOR GENE EXPRESSION data

A NON-PARAMETRIC BAYESIAN CLUSTERING FOR GENE EXPRESSION DAT...

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IEEE Statistical Signal Processing Workshop (SSP)

作者： Wang, Liming Wang, Xiaodong Columbia Univ Dept Elect Engn New York NY 10027 USA

ISBN: (纸本)9781467301831

Clustering is an important data processing tool for interpreting microarray data and genomic network inference. In this paper, we propose a non-parametric Bayesian clustering algorithm based on the hierarchical Dirichlet processes (HDP). The proposed clustering algorithm captures the hierarchical features prevalent in biological data such as the gene express data by introducing a hierarchical structure in the model. We develop a Gibbs sampling algorithm based on the Chinese restaurant metaphor. We conduct experiments on the yeast galactose datasets and yeast cell cycle datasets by comparing our clustering results to the standard results. The proposed clustering algorithm is shown to outperform several popular clustering algorithms by revealing the underlying hierarchical structure of the data. The experiments also show that the proposed clustering algorithm provides more information and reduces the unnecessary clustering fragments than the clustering algorithm based on Dirichlet mixture model.

关键词： Hierarchical Dirichlet processes Dirichlet processes clustering microarray data

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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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A Supervised Solution for Redundant Feature Detection Depending on Instances 2012

A Supervised Solution for Redundant Feature Detection Depend...

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IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)

作者： Zeng, Xue-Qiang Li, Guo-Zheng Tongji Univ Dept Control Sci & Engn Shanghai 201804 Peoples R China

ISBN: (纸本)9781467327466;9781467327459

As a high dimensional problem, analysis of microarray data sets is a challenging task, where many weakly relevant or redundant features hurt generalization performance of classifiers. The previous works used redundant feature detection methods to select discriminative compact gene set, which only considered the relationship among features, not the redundancy of classification ability among features. Here, we propose a novel algorithm named RESI (Redundant fEature Selection depending on Instance), which considers label information in the measure of feature subset redundancy. Experimental results on benchmark data sets show that RESI performs better than the previous state-of-arts algorithms on redundant feature selection methods like mRMR.

关键词： Feature Selection Redundant Feature microarray data

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A novel biclustering approach with iterative optimization to analyze gene expression data

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Advances and Applications in Bioinformatics and Chemistry 2012年第1期5卷 23-59页

作者： Sutheeworapong, Sawannee Ota, Motonori Ohta, Hiroyuki Kinoshita, Kengo Department of Biological Sciences Graduate School of Biosciences and Biotechnology Tokyo Institute of Technology Tokyo Japan Graduate School of Information Sciences Tohoku University Miyagi Japan Institute of Development Aging and Cancer Tohoku University Miyagi Japan Graduate School of Information Sciences Nagoya University Nagoya Japan

Objective: With the dramatic increase in microarray data, biclustering has become a promising tool for gene expression analysis. Biclustering has been proven to be superior over clustering in identifying multifunctional genes and searching for co-expressed genes under a few specific conditions;that is, a subgroup of all conditions. Biclustering based on a genetic algorithm (GA) has shown better performance than greedy algorithms, but the overlap state for biclusters must be treated more systematically. Results: We developed a new biclustering algorithm (binary-iterative genetic algorithm [BIGA]), based on an iterative GA, by introducing a novel, ternary-digit chromosome encoding function. BIGA searches for a set of biclusters by iterative binary divisions that allow the overlap state to be explicitly considered. In addition, the average of the Pearson's correlation coefficient was employed to measure the relationship of genes within a bicluster, instead of the mean square residual, the popular classical index. As compared to the six existing algorithms, BIGA found highly correlated biclusters, with large gene coverage and reasonable gene overlap. The gene ontology (GO) enrichment showed that most of the biclusters are significant, with at least one GO term over represented. Conclusion: BIGA is a powerful tool to analyze large amounts of gene expression data, and will facilitate the elucidation of the underlying functional mechanisms in living organisms. © 2012 Sutheeworapong et al, publisher and licensee Dove Medical Press Ltd.

关键词： Biclustering Genetic algorithm microarray data Pearson's correlation coefficient

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A New Method for Identifying Cancer-Related Gene Association Patterns

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7th International Conference on Intelligent Computing (ICIC)

作者： Wang, Hong-Qiang Xie, Xin-Ping Li, Ding Chinese Acad Sci Hefei Inst Intelligent Machine Intelligent Comp Lab PO 1130 Hefei 230031 Peoples R China Anhui Univ Architecture Dept Math & Phys Hefei 230022 Peoples R China

ISBN: (纸本)9783642245527;9783642245534

Gene association plays important roles in complex genetic pathology of cancer. However, development of methods for finding cancer-related gene associations is still in its infancy. Based on a biological concept of gene association module (GAM) comprising a center gene and its expression-related genes, this paper proposes a gene association detection model called kernel GAM (kGAM). In the model, we assume that the expression of the center gene can be predicted by the expression-related genes. Based on defining a cost function, a kernel ridge regression algorithm is developed to solve the kGAM model. Finally, to identify a compact GAM for a given center gene, a heuristic search procedure is designed. Experimental results on three publicly available gene expression data sets show the effectiveness and efficiency of the proposed kGAM model in identifying cancer-related gene association patterns.

关键词： microarray data kernels ridge regression gene association

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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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Gene Selection using a High-Dimensional Regression Model with microarrays in Cancer Prognostic Studies

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CANCER INFORMATICS 2012年 11卷 29-39页

作者： Kaneko, Shuhei Hirakawa, Akihiro Hamada, Chikuma Tokyo Univ Sci Grad Sch Engn Dept Management Sci Shinjuku Ku 1-3 Kagurazaka Tokyo 1628601 Japan

Mining of gene expression data to identify genes associated with patient survival is an ongoing problem in cancer prognostic studies using microarrays in order to use such genes to achieve more accurate prognoses. The least absolute shrinkage and selection operator (lasso) is often used for gene selection and parameter estimation in high-dimensional microarray data. The lasso shrinks some of the coefficients to zero, and the amount of shrinkage is determined by the tuning parameter, often determined by cross validation. The model determined by this cross validation contains many false positives whose coefficients are actually zero. We propose a method for estimating the false positive rate (FPR) for lasso estimates in a high-dimensional Cox model. We performed a simulation study to examine the precision of the FPR estimate by the proposed method. We applied the proposed method to real data and illustrated the identification of false positive genes.

关键词： cancer prognostic false positive rate gene selection high-dimensional regression microarray data survival analysis

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Modified correlation based technique in micro array data analysis for searching differentially expressed genes

Modified correlation based technique in micro array data ana...

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2012 1st International Conference on Recent Advances in Information Technology, RAIT-2012

作者： Rao, A Chandra Sekhara Somayajulu, D.V.L.N. Banka, Haider Department of CSE ISM Dhanbad Dhanbad India Department of CSE NIT Warangal Warangal India

ISBN: (纸本)9781457706974

Modified correlation Technique has been proposed to analyze the microarray data and to search gene related information. We have used mean absolute deviation as a new approach for the differential expression analysis instead of the existing standard deviation and variances approaches and for calculating co-expression we have used spearmen's correlation as a new technique for micro array analysis. © 2012 IEEE.

关键词： co-expression differential expression microarray data spearmen's correlation

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