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Dimension reduction-based penalized logistic regression for cancer classification using microarray data

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2005年第2期2卷 166-175页

作者： Shen, L Tan, EC Nanyang Technol Univ BioInformat Res Ctr Singapore 637553 Singapore Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore

The use of penalized logistic regression for cancer classification using microarray expression data is presented. Two dimension reduction methods are respectively combined with the penalized logistic regression so that both the classification accuracy and computational speed are enhanced. Two other machine-learning methods, support vector machines and least-squares regression, have been chosen for comparison. It is shown that our methods have achieved at least equal or better results. They also have the advantage that the output probability can be explicitly given and the regression coefficients are easier to interpret. Several other aspects, such as the selection of penalty parameters and components, pertinent to the application of our methods for cancer classification are also discussed.

关键词： dimension reduction penalized logistic regression singular value decomposition partial least squares cancer classification classifier design and evaluation feature evaluation and selection microarray data

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Estimating the number of clusters in microarray data sets based on an information theoretic criterion

Estimating the number of clusters in microarray data sets ba...

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13th IEEE Workshop on Statistical Signal Processing

作者： Nicorici, Daniel Astola, Jaakko Yli-Harja, Olli Tampere Univ Technol Inst Signal Proc FIN-33101 Tampere Finland

ISBN: (纸本)0780394038

This study focuses on an information theoretic approach for estimating the number of clusters K, in microarray data sets. We present an automatic method for estimating K, based on a particular version of the Normalized Maximum Likelihood (NML) model. The strength of the Minimum Description Length (MDL) methods, such as the NML model, in statistical inference is to find the model structure which, in this particular clustering problem, amounts to find the best number of clusters and the best cluster structure for the data. The models are compared using the NML code length. The study introduces a new method for computing the code length of the encoded clustering vector for the data samples, based on the NML model. Experiments with publicly available microarray data sets demonstrate the ability of the new method to find the biologically meaningful clusters.

关键词： number of clusters microarray data minimum description length normalized maximum likelihood

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A microarray data-based semi-kinetic method for predicting quantitative dynamics of genetic networks

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BMC BIOINFORMATICS 2005年第1期6卷 299-299页

作者： Yugi, K Nakayama, Y Kojima, S Kitayama, T Tomita, M Keio Univ Inst Adv Biosci Tsuruoka 9970035 Japan

Background: Elucidating the dynamic behaviour of genetic regulatory networks is one of the most significant challenges in systems biology. However, conventional quantitative predictions have been limited to small networks because publicly available transcriptome data has not been extensively applied to dynamic simulation. Results: We present a microarray data-based semi-kinetic ( MASK) method which facilitates the prediction of regulatory dynamics of genetic networks composed of recurrently appearing network motifs with reasonable accuracy. The MASK method allows the determination of model parameters representing the contribution of regulators to transcription rate from time-series microarray data. Using a virtual regulatory network and a Saccharomyces cerevisiae ribosomal protein gene module, we confirmed that a MASK model can predict expression profiles for various conditions as accurately as a conventional kinetic model. Conclusion: We have demonstrated the MASK method for the construction of dynamic simulation models of genetic networks from time-series microarray data, initial mRNA copy number and first-order degradation constants of mRNA. The quantitative accuracy of the MASK models has been confirmed, and the results indicated that this method enables the prediction of quantitative dynamics in genetic networks composed of commonly used network motifs, which cover considerable fraction of the whole network.

关键词： microarray data Network Motif Ribosomal Protein Gene Genetic Regulatory Network Mask Method

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Clustering and visualization approaches for human cell cycle gene expression data analysis

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2008年第1期47卷 70-84页

作者： Napolitano, F. Ralconi, G. Tagliaferri, R. Ciaramella, A. Staiano, A. Miele, G. Univ Salerno Dept Math & Informat I-84084 Fisciano SA Italy Univ Naples Parthenope Dept Appl Sci I-80133 Naples Italy Univ Naples Federico 2 Dept Phys Sci I-80136 Naples Italy Ist Nazl Fis Nucl Unit Naples I-80125 Naples Italy

In this work a comprehensive multi-step machine learning data mining and data visualization framework is introduced. The different steps of the approach are: preprocessing, clustering, and visualization. A preprocessing based on a Robust Principal Component Analysis Neural Network for feature extraction of unevenly sampled data is used. Then a Probabilistic Principal Surfaces approach combined with an agglomerative procedure based on Fisher's and Negentropy information is applied for clustering and labeling purposes. Furthermore, a Multi-Dimensional Scaling approach for a 2-dimensional data visualization of the clustered and labeled data is used. The method, which provides a user-friendly visualization interface in both 2 and 3 dimensions, can work on noisy data with missing points, and represents an automatic procedure to get, with no a priori assumptions, the number of clusters present in the data. Analysis and identification of genes periodically expressed in a human cancer cell line (HeLa) using cDNA microarrays is carried out as test case. (c) 2007 Elsevier Inc. All rights reserved.

关键词： preprocessing analysis data analysis data visualization microarray data

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Parallel expression profiling of barley-stem rust interactions

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FUNCTIONAL & INTEGRATIVE GENOMICS 2008年第3期8卷 187-198页

作者： Zhang, Ling Castell-Miller, Claudia Dahl, Stephanie Steffenson, Brian Kleinhofs, Andris Washington State Univ Dept Crop & Soil Sci Pullman WA 99164 USA Univ Minnesota Dept Plant Pathol St Paul MN 55108 USA Washington State Univ Sch Mol Biosci Pullman WA 99164 USA

The dominant barley stem rust resistance gene Rpg1 confers resistance to many but not all pathotypes of the stem rust fungus Puccinia graminis f. sp. tritici (Pgt). Transformation of Rpg1 into susceptible cultivar Golden Promise rendered the transgenic plants resistant to Pgt pathotype MCC but not to Pgt pathotype QCC. Our objective was to identify genes that are induced/repressed during the early stages of pathogen infection to elucidate the molecular mechanisms and role of Rpg1 in defense. A messenger ribonucleic acid expression analysis using the 22K Barley1 GeneChip was conducted in all pair-wise combinations of two isolines (cv. Golden Promise and Rpg1 transgenic line G02-448F-3R) and two Pgt pathotypes (MCC and QCC) across six time points. Analysis showed that a total of 34 probe sets exhibited expression pattern differences between Golden Promise (susceptible) and G02-448F-3R (resistant) infected with Pgt-MCC. A total of 14 probe sets exhibited expression pattern differences between Pgt-MCC (avirulent) and Pgt-QCC (virulent) inoculated onto G02-448F-3R. These differentially expressed genes were activated during the early infection process, before the hypersensitive response or fungal growth inhibition occurred. Our analysis provides a list of candidate signaling components, which can be analyzed for function in Rpg1-mediated disease resistance.

关键词： barley disease-resistance expression profiling microarray data rust resistance puccinia graminis f. sp tritici

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A combination of rough-based feature selection and RBF neural network for classification using gene expression data

IEEE TRANSACTIONS ON NANOBIOSCIENCE

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IEEE TRANSACTIONS ON NANOBIOSCIENCE 2008年第1期7卷 91-99页

作者： Chiang, Jung-Hsien Ho, Shing-Hua Natl Cheng Kung Univ Dept Comp Sci & Informat Engn Tainan 701 Taiwan

This paper presents a novel rough-based feature selection method for gene expression data analysis. It can find the relevant features without requiring the number of clusters to be known a priori and identify the centers that approximate to the correct ones. In this paper, we attempt to introduce a prediction scheme that combines the rough-based feature selection method with radial basis function neural network. For further consider the effect of different feature selection methods and classifiers on this prediction process, we use the Naive Bayes and linear support vector machine as classifiers, and compare the performance with other feature selection methods, including information gain and principle component analysis. We demonstrate the performance by several published datasets and the results show that our proposed method can achieve high classification accuracy rate.

关键词： information gain (IG) microarray data principle component analysis radial basis function (RBF) neural network rough-based feature selection

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Cross model validation and optimisation of bilinear regression models (vol 93, pg 1, 2008)

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2008年第1期94卷 87-87页

作者： Gidskehaug, Lars Anderssen, Endre Alsberg, Bjorn K. Norwegian Univ Sci & Technol Dept Chem Chemometr & Bioinformat Grp N-7491 Trondheim Norway

Whenever regression models are optimised, it is important that all optimisation steps are properly validated. Variable selection is one example of parameter estimation that will give overly optimistic models if not included in the validation. There are many examples of reported work where the validation is performed posterior to variable selection, and many have correctly noted that these models are optimistically biased. However, if the availability of samples is limited, separation of the data into a training and validation set may decrease the quality of both the calibration model and the validation. Cross model validation is designed to validate the optimisation by including the variable selection in an extra layer of cross-validation. This means that all available samples are utilised both in the training and for estimating the residual error of the model. Cross model validation poses challenging questions both conceptually and algorithmically, and a presentation of the full work-flow is needed. We present a complete framework including optimisation, validation and calibration of bilinear regression models with variable selection. Several issues are addressed that are important for each separate stage of the analysis, and suggestions for improvements are proposed. The method is validated on a gene expression data set with a low signal-to-noise ratio and a small number of samples. It is shown that many replicates are needed to model these data properly, and that cross model validated variable selection improves both the final calibration model and the associated error estimates. A Matlab toolbox (Mathworks Inc, USA) is available from *** .

关键词： Cross model validation Partial least squares regression PLSR Variable selection Backward elimination microarray data

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A novel approach to feature extraction from classification models based on information gene pairs

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PATTERN RECOGNITION 2008年第6期41卷 1975-1984页

作者： Li, J. Tang, X. Liu, J. Huang, J. Wang, Y. Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Peoples R China

Various microarray experiments are now done in many laboratories, resulting in the rapid accumulation of microarray data in public repositories. One of the major challenges of analyzing microarray data is how to extract and select efficient features from it for accurate cancer classification. Here we introduce a new feature extraction and selection method based on information gene pairs that have significant change in different tissue samples. Experimental results on five public microarray data sets demonstrate that the feature subset selected by the proposed method performs well and achieves higher classification accuracy on several classifiers. We perform extensive experimental comparison of the features selected by the proposed method and features selected by other methods using different evaluation methods and classifiers. The results confirm that the proposed method performs as well as other methods on acute lymphoblastic-acute myeloid leukemia, adenocarcinoma and breast cancer data sets using a fewer information genes and leads to significant improvement of classification accuracy on colon and diffuse large B cell lymphoma cancer data sets. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： feature extraction information gene pair microarray data cancer classification genetic algorithm

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The effects of normalization on the correlation structure of microarray data

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BMC BIOINFORMATICS 2005年第1期6卷 120-120页

作者： Qiu, X Brooks, AI Klebanov, L Yakovlev, N Univ Rochester Dept Biostat & Computat Biol Rochester NY 14642 USA Univ Rochester Funct Genom Ctr Rochester NY 14642 USA Charles Univ Prague Dept Probabil & Stat CZ-18675 Prague Czech Republic

Background: Stochastic dependence between gene expression levels in microarray data is of critical importance for the methods of statistical inference that resort to pooling test-statistics across genes. It is frequently assumed that dependence between genes (or tests) is suffciently weak to justify the proposed methods of testing for differentially expressed genes. A potential impact of between-gene correlations on the performance of such methods has yet to be explored. Results: The paper presents a systematic study of correlation between the t-statistics associated with different genes. We report the effects of four different normalization methods using a large set of microarray data on childhood leukemia in addition to several sets of simulated data. Our findings help decipher the correlation structure of microarray data before and after the application of normalization procedures. Conclusion: A long-range correlation in microarray data manifests itself in thousands of genes that are heavily correlated with a given gene in terms of the associated t-statistics. By using normalization methods it is possible to significantly reduce correlation between the t-statistics computed for different genes. Normalization procedures affect both the true correlation, stemming from gene interactions, and the spurious correlation induced by random noise. When analyzing real world biological data sets, normalization procedures are unable to completely remove correlation between the test statistics. The long- range correlation structure also persists in normalized data.

关键词： microarray data Normalization Procedure Gene Expression Signal Childhood Leukemia Quantile Normalization

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Interactive data analysis and clustering of genomic data

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NEURAL NETWORKS 2008年第2-3期21卷 368-378页

作者： Ciaramella, A. Cocozza, S. Iorio, F. Miele, G. Napolitano, F. Pinelli, M. Raiconi, G. Tagliaferri, R. Univ Salerno Dept Math & Informat I-84084 Fisciano SA Italy Univ Naples Parthenope Dept Appl Sci Ctr Direz I-80143 Naples Italy Univ Naples Federico II CDGU Dept Cellular & Mol Biol & Pathol L Califano Naples Italy Telethon Inst Genet & Med I-80131 Naples Italy Univ Naples Federico II Dept Phys Sci I-80126 Naples Italy

In this work a new clustering approach is used to explore a well-known dataset [Whitfield, M. L., Sherlock, G., Saldanha, A. J., Murray, J. I. Ball, C. A., Alexander, K. E., et al. (2002). Molecular biology of the cell: Vol. 13. Identification. of genes periodically expressed in the human cell cycle and their expression in tumors (pp. 1977-2000)] of time dependent gene expression profiles in human cell cycle. The approach followed by us is realized with a multi-step procedure: after preprocessing, parameters are chosen by using data sub sampling and stability measures;for any used model. several different clustering solutions are obtained by random initialization and are selected basing on a similarity measure and a figure of merit;finally the selected solutions are tuned by evaluating a reliability measure. Three different models for clustering, K-means, Self-organizing Maps and Probabilistic Principal Surfaces are compared. Comparative analysis is carried out by considering: similarity between best solutions obtained through the three methods, absolute distortion value and validation through the use of Gene Ontology (GO) annotations. The GO annotations are used to give significance to the obtained clusters and to compare the results with those obtained in the work cited above. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： clustering microarray data gene ontology clustering assessment and validation data visualization

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