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A statistical framework for expression-based molecular classification in cancer

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY 2002年第4期64卷 717-736页

作者： Parmigiani, G Garrett, ES Anbazhagan, R Gabrielson, E Johns Hopkins Univ Sch Med Div Oncol & Baltimore Baltimore MD 21205 USA

Genome-wide measurement of gene expression is a promising approach to the identification of subclasses of cancer that are currently not differentiable, but potentially biologically heterogeneous. This type of molecular classification gives hope for highly individualized and more effective prognosis and treatment of cancer. Statistically, the analysis of gene expression data from unclassified tumours is a complex hypothesis-generating activity, involving data exploration, modelling and expert elicitation. We propose a modelling framework that can be used to inform and organize the development of exploratory tools for classification. Our framework uses latent categories to provide both a statistical definition of differential expression and a precise, experiment-independent, definition of a molecular profile. It also generates natural similarity measures for traditional clustering and gives probabilistic statements about the assignment of tumours to molecular profiles.

关键词： microarray data analysis mixture distributions molecular classification of cancer

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Mathematical modelling in the post-genome era: understanding genome expression and regulation - a system theoretic approach

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BIOSYSTEMS 2002年第1期65卷 1-18页

作者： Wolkenhauer, O Univ Manchester Control Syst Ctr Dept Elect Engn & Elect Dept Biomol Sci Manchester M60 1QD Lancs England

This paper introduces a mathematical framework for modelling genome expression and regulation. Starting with a philosophical foundation, causation is identified as the principle of explanation of change in the realm of matter. Causation is, therefore, a relationship, not between components, but between changes of states of a system. We subsequently view genome expression (formerly known as 'gene expression') as a dynamic process and model aspects of it as dynamic systems using methodologies developed within the areas of systems and control theory. We begin with the possibly most abstract but general formulation in the setting of category theory. The class of models realised are state-space models, input-output models, autoregressive models or automata. We find that a number of proposed 'gene network' models are, therefore, included in the framework presented here. The conceptual framework that integrates all of these models defines a dynamic system as a family of expression profiles. It becomes apparent that the concept of a 'gene' is less appropriate when considering mathematical models of genome expression and regulation. The main claim of this paper is that we should treat (model) the organisation and regulation of genetic pathways as what they are: dynamic systems. microarray technology allows Lis to generate large sets of time series data and is, therefore, discussed with regard to its use in mathematical modelling of gene expression and regulation. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved.

关键词： genome expression systems biology microarray data analysis gene network modelling

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microarray analysis using bioinformatics analysis audit trails (BAATs)

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COMPTES RENDUS BIOLOGIES 2003年第10-11期326卷 1083-1087页

作者： Bellgard, M Hunter, A Kenworthy, W Murdoch Univ Ctr Bioinformat & Biol Comp Perth WA 6150 Australia

Bioinformatics analysis plays an integrative role in genomics and functional genomics. The ability to conduct quality managed, hypothesis-driven bioinformatics analysis with the plethora of data available is mandatory. Biological interpretation of this data is dependent on versions of databases, programs and the parameters used. Thus, tracking and auditing the analyses process is important. This paper outlines what we term Bioinformatics analysis Audit Trails (BAATs) and describes YABI, a bioinformatics environment that implements BAATs. YABI can incorporate most bioinformatics tools within the same environment, making it a valuable resource. (C) 2003 Published by Elsevier SAS on behalf of Academie des sciences.

关键词： audit trails bioinformatics analysis pipelines comparative genomic sequence analysis microarray data analysis

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data-dependent kernel machines for microarray data classification

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2007年第4期4卷 583-595页

作者： Xiong, Huilin Zhang, Ya Chen, Xue-Wen Univ Kansas Dept Elect Engn & Comp Sci Lawrence KS 66045 USA Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

One important application of gene expression analysis is to classify tissue samples according to their gene expression levels. Gene expression data are typically characterized by high dimensionality and small sample size, which makes the classification task quite challenging. In this paper, we present a data-dependent kernel for microarray data classification. This kernel function is engineered so that the class separability of the training data is maximized. A bootstrapping-based resampling scheme is introduced to reduce the possible training bias. The effectiveness of this adaptive kernel for microarray data classification is illustrated with a k-Nearest Neighbor (KNN) classifier. Our experimental study shows that the data-dependent kernel leads to a significant improvement in the accuracy of KNN classifiers. Furthermore, this kernel-based KNN scheme has been demonstrated to be competitive to, if not better than, more sophisticated classifiers such as Support Vector Machines (SVMs) and the Uncorrelated Linear Discriminant analysis (ULDA) for classifying gene expression data.

关键词： microarray data analysis cancer classification kernel machines kernel optimization bootstrapping resampling

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2004年第4期1卷 181-190页

作者： Ye, JP Li, T Xiong, T Janardan, R Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA Florida Int Univ Sch Comp Sci Miami FL 33199 USA Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high (in the thousands) compared to the number of data samples (in the tens or low hundreds);that is, the data dimension is large compared to the number of data points (such data is said to be undersampled). To cope with performance and accuracy problems associated with high dimensionality, it is commonplace to apply a preprocessing step that transforms the data to a space of significantly lower dimension with limited loss of the information present in the original data. Linear Discriminant analysis (LDA) is a well-known technique for dimension reduction and feature extraction, but it is not applicable for undersampled data due to singularity problems associated with the matrices in the underlying representation. This paper presents a dimension reduction and feature extraction scheme, called Uncorrelated Linear Discriminant analysis (ULDA), for undersampled problems and illustrates its utility on gene expression data. ULDA employs the Generalized Singular Value Decomposition method to handle undersampled data and the features that it produces in the transformed space are uncorrelated, which makes it attractive for gene expression data. The properties of ULDA are established rigorously and extensive experimental results on gene expression data are presented to illustrate its effectiveness in classifying tissue samples. These results provide a comparative study of various state-of-the-art classification methods on well-known gene expression data sets.

关键词： microarray data analysis discriminant analysis generalized singular value decomposition classification

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OVA scheme vs. single machine approach in feature selection for microarray datasets

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6th Industrial Conference on data Mining (ICDM 2006)

作者： Ooi, Chia Huey Chetty, Madhu Teng, Shyh Wei Monash Univ Gippsland Sch Informat Technol Churchill Vic 3842 Australia

ISBN: (纸本)3540360360

The large number of genes in microarray data makes feature selection techniques more crucial than ever. From rank-based filter techniques to classifier-based wrapper techniques, many studies have devised their own feature selection techniques for microarray datasets. By combining the OVA (one-vs.-all) approach and differential prioritization in our feature selection technique, we ensure that class-specific relevant features are selected while guarding against redundancy in predictor set at the same time. In this paper we present the OVA version of our differential prioritization-based feature selection technique and demonstrate how it works better than the original SMA (single machine approach) version.

关键词： molecular classification microarray data analysis feature selection

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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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Restricted Boltzmann Machines for Unsupervised Feature Selection with Partial Least Square Feature Extractor for microarray datasets 9

Restricted Boltzmann Machines for Unsupervised Feature Selec...

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9th International Conference on Advanced Computer Science and Information Systems (ICACSIS)

作者： Sutawika, Lintang Adyuta Wasito, Ito Univ Indonesia Fac Comp Sci Depok Indonesia

ISBN: (纸本)9781538631720

Feature selection is a key component in microarray data analysis. This is due to the fact that microarray datasets consists of features that are far exceed the number of instances. High dimensional data are also known to contain significant amount of noise and irrelevant variables that do not contribute to classification tasks and may even hinder classification performance. In this paper, a feature selection method which consists of two stages is proposed. At the first step, feature selection is done through a stacked Restricted Boltzmann Machines by means of comparing the error between reconstructed data and the original data. The next stage will use Partial Least Square to extract synthesis features from the previously selected features that will be then used for classification. The performance of the proposed method is done through the classification of ten microarray datasets that are widely used. The proposed model is able to out perform state-of-the-art in 2 datasets, namely 82.11% for GLIOMA and 72.39% for Breast datasets.

关键词： restricted boltzmann machine deep learning microarray data analysis gene expression

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4th International Workshop on Algorithms in Bioinformatics (WABI 2004)

关键词： microarray data analysis discriminant analysis generalized singular value decomposition classification

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Mining potential information for Multiclass microarray data using Centroid-based Dimension Reduction

Mining potential information for Multiclass Microarray Data ...

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IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Guo, Shun Guo, Donghui Xiamen Univ Dept Elect Engn Fujian 361005 Peoples R China Xiamen Univ IC Design & IT Res Ctr Fujian Prov Fujian 361005 Peoples R China

ISBN: (纸本)9781467395984

In this paper, we propose a novel dimension reduction algorithm that implements an information fusion of Centroid-based feature selection and partial least squares (PLS) based feature extraction. This paper focuses on mining the potential information hidden in multiclass microarray data and interpreting the results provided by the potential information. Firstly, a centroid concept has been introduced to define the objective function of feature selection. In order to obtain the sparse solution, logistic regression with L1 regularization has been incorporated into the objective function. The Centroid-based feature selection is then proposed to solve the optimization problem. By using the One-Versus-All (OVA) techniques, the Centroid-based feature selection is extended to solve multiclass problems. Secondly, we perform feature important analysis on microarray data by Centroid-based feature selection to determine the information feature subset (biomarkers). Finally, PLS-based feature extraction is conducted on the selected feature subset to extract the features that best reflect the nature of classification. The proposed algorithm is compared with three state-of-the-art algorithms using eight multiclass microarray datasets. The experimental results demonstrate that the proposed algorithm performs effectively and is competitive. Furthermore, mining the potential information of the microarray dataset improves the interpretability of the results.

关键词： Dimension reduction Centroid L1 regularization partial least squares (PLS) microarray data analysis

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