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A pattern-oriented specification of gene network inference processes

COMPUTERS IN BIOLOGY AND MEDICINE 2013年第10期43卷 1415-1427页

作者： Trepode, Nestor W. de Farias, Clever R. G. Barrera, Junior Univ Sao Paulo Dept Comp Sci & Math DCM Fac Philosophy Sci & Letters Ribeirao Preto FFCLR BR-14040901 Ribeirao Preto SP Brazil

Patterns have been widely used in Computer Science. A pattern describes a generic solution to an existing problem in a more readable and accessible form. A pattern-oriented process specification consists of a generic and abstract description of a process. This paper presents a pattern-oriented specification of a genetic regulatory network inference process performed from microarray data and prior biological knowledge. The proposed specification was conceived based on prior work on gene inference networks. The adequacy of the proposed solution was then evaluated with respect to modern tendencies of the genes network inference literature. (c) 2013 Elsevier Ltd. All rights reserved.

关键词： Genetic regulatory networks Dynamical system identification Gene network inference microarray data analysis Process modeling Patterns

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ICP: A novel approach to predict prognosis of prostate cancer with inner-class clustering of gene expression data

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COMPUTERS IN BIOLOGY AND MEDICINE 2013年第10期43卷 1363-1373页

作者： Kim, Hyunjin Ahn, Jaegyoon Park, Chihyun Yoon, Youngmi Park, Sanghyun Yonsei Univ Dept Comp Sci Seoul 120749 South Korea Gachon Univ Dept Comp Engn Inchon South Korea

Prostate cancer has heterogeneous characteristics. For that reason, even if tumors appear histologically similar to each other, there are many cases in which they are actually different, based on their gene expression levels. A single tumor may have multiple expression levels with both high-risk cancer genes and low-risk cancer genes. We can produce more useful models for stratifying prostate cancers into high-risk cancer and low-risk cancer categories by considering the range in each class through inner-class clustering. In this paper, we attempt to classify cancers into high-risk (aggressive) prostate cancer and low-risk (non-aggressive) prostate cancer using ICP (Inner-class Clustering and Prediction). Our model classified more efficiently than the models of the algorithms used for comparison. After discovering a number of genes linked to prostate cancer from the gene pairs used in our classification, we discovered that the proposed method can be used to find new unknown genes and gene pairs which distinguish between high-risk cancer and low-risk cancer. (c) 2013 Elsevier Ltd. All rights reserved.

关键词： Classification Clustering Bioinformatics microarray data analysis Prognosis Prostate cancer

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Statistical properties of the quantile normalization method for density curve alignment

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MATHEMATICAL BIOSCIENCES 2013年第2期242卷 129-142页

作者： Gallon, Santiago Loubes, Jean-Michel Maza, Elie Univ Antioquia Dept Matemat & Estadist Fac Ciencias Econ Medellin 050010 Colombia Univ Toulouse 3 Inst Math Toulouse F-31062 Toulouse 9 France UMR 990 INRA INP ENSAT Genom & Biotechnol Fruit Lab Toulouse France

The article investigates the large sample properties of the quantile normalization method by Bolstad et al. (2003) [4] which has become one of the most popular methods to align density curves in microarray data analysis. We prove consistency of this method which is viewed as a particular case of the structural expectation procedure for curve alignment, which corresponds to a notion of barycenter of measures in the Wasserstein space. Moreover, we show that, this method fails in some case of mixtures, and we propose a new methodology to cope with this issue. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Curve registration Manifold registration microarray data analysis Normalization Order statistics Structural expectation Wasserstein distance

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A variational Bayesian framework for group feature selection

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2013年第6期4卷 609-619页

作者： Subrahmanya, Niranjan Shin, Yung C. ExxonMobil Res & Engn Co Annandale NJ USA Purdue Univ W Lafayette IN 47907 USA

In many machine learning and pattern analysis applications, grouping of features during model development and the selection of a small number of relevant groups can be useful to improve the interpretability of the learned parameters. Although this problem has been receiving a significant amount of attention lately, most of the approaches require the manual tuning of one or more hyper-parameters. In order to overcome this drawback, this work presents a novel hierarchical Bayesian formulation of a generalized linear model and estimates the posterior distribution of the parameters and hyper-parameters of the model within a completely Bayesian paradigm based on variational inference. All the required computations are analytically tractable. The performance and applicability of the proposed framework is demonstrated on synthetic and real world examples.

关键词： Feature group selection Hierarchical bayes microarray data analysis Sensor selection Variational bayes Wavelength selection

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Sparse Manifold Clustering and Embedding to discriminate gene expression profiles of glioblastoma and meningioma tumors

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COMPUTERS IN BIOLOGY AND MEDICINE 2013年第11期43卷 1863-1869页

作者： Garcia-Gomez, Juan M. Gomez-Sanchis, Juan Escandell-Montero, Pablo Fuster-Garcia, Elies Soria-Olivas, Emilio Univ Politecn Valencia Biomed Informat Grp IBIME ITACA Valencia 46022 Spain Univ Valencia Dept Elect Engn Intelligent Data Anal Lab E-46100 Valencia Spain Hosp La Fe IIS Grp Invest Biomed Imagen GIBI230 Valencia Spain

Sparse Manifold Clustering and Embedding (SMCE) algorithm has been recently proposed for simultaneous clustering and dimensionality reduction of data on nonlinear manifolds using sparse representation techniques. In this work, SMCE algorithm is applied to the differential discrimination of Glioblastoma and Meningioma Tumors by means of their Gene Expression Profiles. Our purpose was to evaluate the robustness of this nonlinear manifold to classify gene expression profiles, characterized by the high-dimensionality of their representations and the low discrimination power of most of the genes. For this objective, we used SMCE to reduce the dimensionality of a preprocessed dataset of 35 single-labeling cDNA microarrays with 11500 original clones. Afterwards, supervised and unsupervised methodologies were applied to obtain the classification model: the former was based on linear discriminant analysis, the later on clustering using the SMCE embedding data. The results obtained using both approaches showed that all (100%) the samples could be correctly classified and the results of all repetitions but one formed a compatible cluster of predictive labels. Finally, the embedding dimensionality of the dataset extracted by SMCE revealed large discrimination margins between both classes. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Manifolds Automatic classification microarray data analysis Medical applications Bioinformatics

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Comparison of Feature Selection Methods for Cross-Laboratory microarray analysis

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2013年第3期10卷 593-604页

作者： Liu, Hsi-Che Peng, Pei-Chen Hsieh, Tzung-Chien Yeh, Ting-Chi Lin, Chih-Jen Chen, Chien-Yu Hou, Jen-Yin Shih, Lee-Yung Liang, Der-Cherng Mackay Mem Hosp Mackay Med Coll New Taipei Taiwan Mackay Mem Hosp Div Pediat Hematol Oncol New Taipei Taiwan Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei 10764 Taiwan Natl Taiwan Univ Dept Bioind Mech Engn Taipei 10764 Taiwan Chang Gung Mem Hosp Dept Internal Med Div Hematol Oncol Taipei 10591 Taiwan Chang Gung Univ Sch Med Tao Yuan Taiwan

The amount of gene expression data of microarray has grown exponentially. To apply them for extensiVe studies, integrated analysis of cross-laboratory (cross-lab) data'becomes a trend, and thus, choosing an appropriate feature selection method is an essential issue. This paper focuses on,feature selection for Affymetrix.(Affy) microarray studies across differentlabs. We investigate four feature selection methods: t-test, significance analysis of microarrays (SAM), rank products'(RP), and random forest (RF)..The four methods are applied'to acute lymphoblastic leukemia, acute myeloid leukemia, breast cancer, and lung cancer Affy datawhich consist of three cross-lab data sets each. We utilize a rank-based normalization method to reduce thetias from cross-lab-data sets. Training on one data set or two combined data sets to test the remaining data set(s) are both' considered, Balanced accuracy is used for prediction evaluation. This study provides comprehensive comparisons of the four feature selection methods in cross-lab microarray analysis. Results show, that SAM has the best classification performance. RF also getS high classification accuracy, but it is not as stable as SAM. The most naive method is t-test, but its performance is the worst among the four methods. In thiS study, we further discuss the influence from the number of training samples, the number of selected genes, and the issue of unbalanced data sets.

关键词： microarray data analysis feature selection cancer cross-laboratory experiment

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A graph spectrum based geometric biclustering algorithm

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JOURNAL OF THEORETICAL BIOLOGY 2013年 317卷 200-211页

作者： Wang, Doris Z. Yan, Hong City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China

Biclustering is capable of performing simultaneous clustering on two dimensions of a data matrix and has many applications in pattern classification. For example, in microarray experiments, a subset of genes is co-expressed in a subset of conditions, and biclustering algorithms can be used to detect the coherent patterns in the data for further analysis of function. In this paper, we present a graph spectrum based geometric biclustering (GSGBC) algorithm. In the geometrical view, biclusters can be seen as different linear geometrical patterns in high dimensional spaces. Based on this, the modified Hough transform is used to find the Hough vector (HV) corresponding to sub-bicluster patterns in 2D spaces. A graph can be built regarding each HV as a node. The graph spectrum is utilized to identify the eigengroups in which the sub-biclusters are grouped naturally to produce larger biclusters. Through a comparative study, we find that the GSGBC achieves as good a result as GBC and outperforms other kinds of biclustering algorithms. Also, compared with the original geometrical biclustering algorithm, it reduces the computing time complexity significantly. We also show that biologically meaningful biclusters can be identified by our method from real microarray gene expression data. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Geometric biclustering Hough transform Graph spectra microarray data analysis

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A Bayesian decision theoretic approach to directional multiple hypotheses problems

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JOURNAL OF MULTIVARIATE analysis 2013年 120卷 205-215页

作者： Bansal, Naveen K. Miescke, Klaus J. Marquette Univ Dept Math Stat & Comp Sci Milwaukee WI 53201 USA Univ Illinois Dept Math Stat & Comp Sci Chicago IL 60680 USA

A multiple hypothesis problem with directional alternatives is considered in a decision theoretic framework. Skewness in the alternatives is considered, and it is shown that this skewness permits the Bayes rules to possess certain advantages when one direction of the alternatives is more important or more probable than the other direction. Bayes rules subject to constraints on certain directional false discovery rates are obtained, and their performances are compared with a traditional FDR rule through simulation. We also analyzed a gene expression data using our methodology, and compare the results to that of a FDR method. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Directional alternatives False discovery rate microarray data analysis MicroRNA Multiple hypotheses

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Identification of potential biomarkers from microarray experiments using multiple criteria optimization

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CANCER MEDICINE 2013年第2期2卷 253-265页

作者： Sanchez-Pena, Matilde L. Isaza, Clara E. Perez-Morales, Jaileene Rodriguez-Padilla, Cristina Castro, Jose M. Cabrera-Rios, Mauricio Univ Puerto Rico Mayaguez Dept Ind Engn Bio IE Lab Mayaguez PR 00680 USA Univ Autonoma Nuevo Leon Immunol & Virol Lab Monterrey Mexico Ohio State Univ Columbus OH 43210 USA

microarray experiments are capable of determining the relative expression of tens of thousands of genes simultaneously, thus resulting in very large databases. The analysis of these databases and the extraction of biologically relevant knowledge from them are challenging tasks. The identification of potential cancer biomarker genes is one of the most important aims for microarray analysis and, as such, has been widely targeted in the literature. However, identifying a set of these genes consistently across different experiments, researches, microarray platforms, or cancer types is still an elusive endeavor. Besides the inherent difficulty of the large and nonconstant variability in these experiments and the incommensurability between different microarray technologies, there is the issue of the users having to adjust a series of parameters that significantly affect the outcome of the analyses and that do not have a biological or medical meaning. In this study, the identification of potential cancer biomarkers from microarray data is casted as a multiple criteria optimization (MCO) problem. The efficient solutions to this problem, found here through data envelopment analysis (DEA), are associated to genes that are proposed as potential cancer biomarkers. The method does not require any parameter adjustment by the user, and thus fosters repeatability. The approach also allows the analysis of different microarray experiments, microarray platforms, and cancer types simultaneously. The results include the analysis of three publicly available microarray databases related to cervix cancer. This study points to the feasibility of modeling the selection of potential cancer biomarkers from microarray data as an MCO problem and solve it using DEA. Using MCO entails a new optic to the identification of potential cancer biomarkers as it does not require the definition of a threshold value to establish significance for a particular gene and the selection of a normalization procedu

关键词： Cancer biomarkers cervical cancer data envelopment analysis microarray data analysis multiple criteria optimization

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Mining Breast Cancer Genetic data

Mining Breast Cancer Genetic Data

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9th International Conference on Natural Computation (ICNC)

作者： Mansour, Nashat Zardout, Rouba El-Sibai, Mirvat Lebanese Amer Univ Dept Comp Sci & Math Beirut Lebanon Lebanese Amer Univ Dept Nat Sci Beirut Lebanon

ISBN: (纸本)9781467347143

Analyzing breast cancer gene expression data is a very challenging problem due to the large amount of genes examined. Computational techniques have proved reliable to make sense of large amounts of data like the data obtained from microarray analysis. In this study, we present a method to find a clustering pattern of the genes involved in breast cancer. We design a growing hierarchical self-organizing map (GHSOM) to mine gene microarray data. We have applied GHSOM to 24,481 genes of DNA microarray of breast tumor samples. Our results have revealed 17 genes that are likely to be correlated with four breast cancer marker genes.

关键词： Bioinformatics Growing hierarchical self-organizing map Breast cancer microarray data analysis Clustering Self-organizing map

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