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TC-VGC: A Tumor Classification System using Variations in Genes' Correlation

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2011年第3期104卷 E87-E101页

作者： Shin, Eunji Yoon, Youngmi Ahn, Jaegyoon Park, Sanghyun Yonsei Univ Dept Comp Sci Seoul 120749 South Korea Gachon Univ Med & Sci Div Informat Technol Inchon 5342 South Korea

Classification analysis of microarray data is widely used to reveal biological features and to diagnose various diseases, including cancers. Most existing approaches improve the performance of learning models by removing most irrelevant and redundant genes from the data. They select the marker genes which are expressed differently in normal and tumor tissues. These techniques ignore the importance of the complex functional-dependencies between genes. In this paper, we propose a new method for cancer classification which uses distinguished variations of gene-gene correlation in two sample groups. The cancer specific genetic network composed of these gene pairs contains many literature-curated prostate cancer genes, and we were successful in identifying new candidate prostate cancer genes inferred by them. Furthermore, this method achieved a high accuracy with a small number of genes in cancer classification. (C) 2011 Elsevier Ireland Ltd. All rights reserved.

关键词： Tumor classification microarray data analysis Gene-gene correlation Cancer specific genetic network

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analysis of DNA microarray expression data

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BEST PRACTICE & RESEARCH CLINICAL HAEMATOLOGY 2009年第2期22卷 271-282页

作者： Simon, Richard NCI Biometr Res Branch Div Canc Treatment & Diag Bethesda MD 20892 USA

DNA microarrays are powerful tools for studying biological mechanisms and for developing prognostic and predictive classifiers for identifying the patients who require treatment and are best candidates for specific treatments. Because microarrays produce so much data from each specimen, they offer great opportunities for discovery and great dangers or producing misleading claims. microarray based studies require clear objectives for selecting cases and appropriate analysis methods. Effective analysis of microarray data, where the number of measured variables is orders of magnitude greater than the number of cases, requires specialized statistical methods which have recently been developed. Recent literature reviews indicate that serious problems of analysis exist a substantial proportion of publications. This manuscript attempts to provide a non-technical summary of the key principles of statistical design and analysis for studies that utilize microarray expression profiling. Published by Elsevier Ltd.

关键词： bioinformatics biomarkers gene expression signatures microarray data analysis

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Interpretable gene expression classifier with an accurate and compact fuzzy rule base for microarray data analysis

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BIOSYSTEMS 2006年第3期85卷 165-176页

作者： Ho, Shinn-Ying Hsieh, Chih-Hung Chen, Hung-Ming Huang, Hui-Ling Natl Chiao Tung Univ Dept Biol Sci & Technol Hsinchu Taiwan Natl Chiao Tung Univ Inst Bioinformat Hsinchu Taiwan Feng Chia Univ Inst Comp Sci & Informat Engn Taichung 40724 Taiwan Jin Wen Inst Technol Dept Informat Management Taipei Taiwan

An accurate classifier with linguistic interpretability using a small number of relevant genes is beneficial to microarray data analysis and development of inexpensive diagnostic tests. Several frequently used techniques for designing classifiers of microarray data, such as support vector machine, neural networks, k-nearest neighbor, and logistic regression model, suffer from low interpretabilities. This paper proposes an interpretable gene expression classifier (named iGEC) with an accurate and compact fuzzy rule base for microarray data analysis. The design of iGEC has three objectives to be simultaneously optimized: maximal classification accuracy, minimal number of rules, and minimal number of used genes. An "intelligent" genetic algorithm IGA is used to efficiently solve the design problem with a large number of tuning parameters. The performance of iGEC is evaluated using eight commonly-used data sets. It is shown that iGEC has an accurate, concise, and interpretable rule base (1.1 rules per class) on average in terms of test classification accuracy (87.9%), rule number (3.9), and used gene number (5.0). Moreover, iGEC not only has better performance than the existing fuzzy rule-based classifier in terms of the above-mentioned objectives, but also is more accurate than some existing non-rule-based classifiers. (c) 2006 Elsevier Ireland Ltd. All rights reserved.

关键词： fuzzy classifier gene expression intelligent genetic algorithm microarray data analysis pattern recognition

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Goal Driven analysis of cDNA microarray data

Goal Driven Analysis of cDNA Microarray Data

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IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

作者： Pan, Youlian Zou, Jitao Huang, Yi Liu, Ziying Phan, Sieu Famili, Fazel A. Natl Res Council Canada Inst Informat Technol Knowledge Discovery Grp 1200 Montreal Rd Ottawa ON K1A 0R6 Canada Natl Res Council Canada Inst Plant Biotechnol Saskatoon SK S7N 0W9 Canada

ISBN: (纸本)9781424427567

microarray technology has been used extensively for high throughput gene expression studies. Many bioinformatics tools are available for analysis of microarray data. In the data mining process, it is important to be goal oriented so that a set of proper tools can be assembled for the targeted knowledge discovery process. In this paper, we tackle this issue by using a microarray dataset from Brassica endosperm together with EST data to validate our process. We were most interested in which genes are highly expressed in Brassica endosperm and their variations and functions over various stages in embryo development. We also performed gene characterization based on gene ontology analysis. Our results indicate that designing a specific data mining workflow that considers both the log ratio and signal intensity enhances knowledge discovery process. Through this approach, we were able to rind the regulatory relationship between two most important transcription factors, LEC1 and WRI1 in the endosperm of Brassica napus.

关键词： microarray data analysis gene expression differentially expressed genes transcriptional regulation

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The microarray data analysis Process: From Raw data to Biological Significance

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NeuroRx 2006年第3期3卷 373-383页

作者： Olson, N. Eric VizX Labs Seattle WA United States

Summary: Despite advances in microarray technology that have led to increased reproducibility and substantial reductions in the cost of microarrays, the successful use of this technology is still elusive for many researchers, and microarray data analysis in particular presents a substantial bottleneck for many biomedical researchers. There are many reasons for this, including the expense of and a lack of adequate training in the use of analysis software. An additional reason is that microarray data analysis has largely been treated in the past as a set of separate steps, with the majority of emphasis being placed on statistical analysis and visualization of the data. For many biomedical researchers determining the biological significance of the data has been the greatest challenge and in the last several years more emphasis has been placed on this aspect of the analysis process. Despite this broadening of the scope of analysis there are still several aspects of the process that continue to be neglected, including additional related and interdependent aspects, such as experimental design, data accessibility, and platform selection. Though not traditionally thought of as integral to the data analysis process, these factors have profound effects on the analysis process. This article will discuss the importance of these additional aspects, as well as statistical analysis and determination of biological significance of microarray data. A summary of currently available software options will also be presented with a focus on the aspects discussed. © 2006 The American Society for Experimental NeuroTherapeutics, Inc.

关键词： biological significance clustering microarray microarray data analysis statistical analysis

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microarray data Classifier Consisting of k-Top-Scoring Rank-Comparison Decision Rules With a Variable Number of Genes

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS 2010年第2期40卷 216-226页

作者： Yoon, Youngmi Bien, Sangjay Park, Sanghyun Gachon Univ Med & Sci Dept Informat Technol Inchon 406799 South Korea Seoul Natl Univ Interdisciplinary Program Bioinformat Seoul 151742 South Korea Yonsei Univ Dept Comp Sci Seoul 120749 South Korea

microarray experiments generate quantitative expression measurements for thousands of genes simultaneously, which is useful for phenotype classification of many diseases. Our proposed phenotype classifier is an ensemble method with k-top-scoring decision rules. Each rule involves a number of genes, a rank comparison relation among them, and a class label. Current classifiers, which are also ensemble methods, consist of k-top-scoring decision rules. Some of these classifiers fix the number of genes in each rule as a triple or a pair. In this paper, we generalize the number of genes involved in each rule. The number of genes in each rule ranges from 2 to N, respectively. Generalizing the number of genes increases the robustness and the reliability of the classifier for the class prediction of an independent sample. Our algorithm saves resources by combining shorter rules in order to build a longer rule. It converges rapidly toward its high-scoring rule list by implementing several heuristics. The parameter k is determined by applying leave-one-out cross validation to the training dataset.

关键词： data mining knowledge-based data mining microarray data analysis microarray data classification

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Multi-step dimensionality reduction and semi-supervised graph-based tumor classification using gene expression data

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2010年第3期50卷 181-191页

作者： Gui, Jie Wang, Shu-Lin Lei, Ying-Ke Chinese Acad Sci Intelligent Comp Lab Hefei Inst Intelligent Machines Hefei 230031 Anhui Peoples R China Univ Sci & Technol China Dept Automat Hefei 230026 Anhui Peoples R China Hunan Univ Sch Comp & Commun Changsha 410082 Hunan Peoples R China Inst Elect Engn Hefei 230037 Anhui Peoples R China

Objective Both supervised methods and unsupervised methods have been widely used to solve the tumor classification problem based on gene expression profiles This paper introduces a semi-supervised graph-based method for tumor classification Feature extraction plays a key role in tumor classification based on gene expression profiles and can greatly improve the performance of a classifier In this paper we propose a novel multi-step dimensionality reduction method for extracting tumor-related features Methods and materials First the Wilcoxon rank-sum test is used for gene selection Then gene ranking and discrete cosine transform are combined with principal component analysis for feature extraction Finally the performance is evaluated by semi-supervised learning algorithms Results To show the validity of the proposed method we apply it to classify four tumor datasets involving various human normal and tumor tissue samples The experimental results show that the proposed method is efficient and feasible Compared with other methods our method can achieve relatively higher prediction accuracy Particularly it is found that semi-supervised method is superior to support vector machines in classification performance Conclusions The proposed approach can effectively improve the performance of tumor classification based on gene expression profiles This work is a meaningful attempt to explore and apply multi-step dimensionality reduction and semi-supervised learning methods in the field of tumor classification Considering the high classification accuracy there should be much room for the application of multi-step dimensionality reduction and semi-supervised learning methods to perform tumor classification (C) 2010 Elsevier B V All rights reserved

关键词： Multi-step dimensionality reduction Gene ranking Discrete cosine transform Principal component analysis Semi-supervised learning microarray data analysis Tumor diagnosis

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Advances in metaheuristics for gene selection and classification of microarray data

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BRIEFINGS IN BIOINFORMATICS 2010年第1期11卷 127-141页

作者： Duval, Beatrice Hao, Jin-Kao Univ Angers F-49045 Angers 01 France

Gene selection aims at identifying a (small) subset of informative genes from the initial data in order to obtain high predictive accuracy for classification. Gene selection can be considered as a combinatorial search problem and thus be conveniently handled with optimization methods. In this article, we summarize some recent developments of using metaheuristic-based methods within an embedded approach for gene selection. In particular, we put forward the importance and usefulness of integrating problem-specific knowledge into the search operators of such a method. To illustrate the point, we explain how ranking coefficients of a linear classifier such as support vector machine (SVM) can be profitably used to reinforce the search efficiency of Local Search and Evolutionary Search metaheuristic algorithms for gene selection and classification.

关键词： microarray data analysis gene selection classification local search genetic algorithm memetic algorithm

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An evolutionary approach for gene selection and classification of microarray data based on SVM error-bound theories

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BIOSYSTEMS 2010年第1期100卷 39-46页

作者： Debnath, Rameswar Kurita, Takio AIST Neurosci Res Inst Tsukuba Ibaraki 3058568 Japan

microarrays have thousands to tens-of-thousands of gene features, but only a few hundred patient samples are available. The fundamental problem in microarray data analysis is identifying genes whose disruption causes congenital or acquired disease in humans. In this paper, we propose a new evolutionary method that can efficiently select a subset of potentially informative genes for support vector machine (SVM) classifiers. The proposed evolutionary method uses SVM with a given subset of gene features to evaluate the fitness function, and new subsets of features are selected based on the estimates of generalization error of SVMs and frequency of occurrence of the features in the evolutionary approach. Thus, in theory, selected genes reflect to some extent the generalization performance of SVM classifiers. We compare our proposed method with several existing methods and find that the proposed method can obtain better classification accuracy with a smaller number of selected genes than the existing methods. (C) 2010 Elsevier Ireland Ltd. All rights reserved.

关键词： Support vector machine Generalization error-bound Feature selection Evolutionary algorithm microarray data analysis

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Effective PCA for high-dimension, low-sample-size data with singular value decomposition of cross data matrix

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JOURNAL OF MULTIVARIATE analysis 2010年第9期101卷 2060-2077页

作者： Yata, Kazuyoshi Aoshima, Makoto Univ Tsukuba Inst Math Ibaraki 3058571 Japan Univ Tsukuba Grad Sch Pure & Appl Sci Ibaraki 3058571 Japan

In this paper, we propose a new methodology to deal with PCA in high-dimension, low-sample-size (HDLSS) data situations. We give an idea of estimating eigenvalues via singular values of a cross data matrix. We provide consistency properties of the eigenvalue estimation as well as its limiting distribution when the dimension d and the sample size n both grow to infinity in such a way that n is much lower than d. We apply the new methodology to estimating PC directions and PC scores in HDLSS data situations. We give an application of the findings in this paper to a mixture model to classify a dataset into two clusters. We demonstrate how the new methodology performs by using HDLSS data from a microarray study of prostate cancer. (c) 2010 Elsevier Inc. All rights reserved.

关键词： Consistency Eigenvalue distribution HDLSS microarray data analysis Mixture model Principal component analysis Singular value

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