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39th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Kourou, Konstantina Papaloukas, Costas Fotiadis, Dimitrios I. Univ Ioannina Dept Mat Sci & Engn Unit Med Technol & Intelligent Informat Syst GR-45110 Ioannina Greece Fdn Res & Technol Hellas FORTH Iraklion Greece Univ Ioannina Dept Biol Applicat & Technol GR-45110 Ioannina Greece

ISBN: (纸本)9781509028092

We propose a meta-analysis scheme for identifying differentially expressed genes in Oral Squamous Cell Carcinoma (OSCC) from different microarray studies. We detect a subset of relevant features and further classify samples under two experimental conditions (i.e healthy and cancer samples) for better patient stratification. A well-established meta-analysis method is adopted and gene expression data sets are derived from a public functional genomics data repository. Our primary aim is the accurate identification of up-and down-regulated genes in order to extract valuable biological information concerning the changes in expression between healthy and cancer samples. According to our results and the extracted informative gene list, a high classification accuracy of healthy and OSCC tumors is achieved with as few genes as possible. Furthermore, the proposed scheme implies that the combination of datasets from different origins may reduce the estimated percentage of false predictions, while the power of gene identification and disease classification is increased.

关键词： differential expression disease classification informative genes meta-analysis microarray data oral cancer

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A gene selection method for microarray data based on risk genes

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第11期38卷 14065-14071页

作者： Wong, Tzu-Tsung Chen, Ding-Qun Natl Cheng Kung Univ Inst Informat Management Tainan 701 Taiwan

Many gene selection methods have been proposed to select a subset of genes that can have a high prediction accuracy for cancer classification, and most set the same preference for all genes. However, many biological reports have pointed out that mutated or flawed genes, named as risk genes, can be one of the major causes of a specific disease. This study proposes a gene selection method based on the risk genes found in biological reports. The information provided by risk genes can reduce the time complexity for gene selection and increase the accuracy of cancer classification. This gene selection method is composed of two stages. Since all risk genes must be chosen, the first stage is to remove the genes that have similar expression levels or functions to risk genes. The next stage is to perform gene selection and gene replacement based on the results of a process that divides the remaining genes into clusters. Based on the test results from four microarray data sets, our gene selection method outperforms those proposed by previous studies, and genes that have the potential to be new risk genes are presented. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Cancer classification Clustering analysis Gene selection microarray data Risk genes

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Analysis of gene microarray data in a soft computing framework

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APPLIED SOFT COMPUTING 2011年第6期11卷 4152-4160页

作者： Maulik, Ujjwal Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 W Bengal India

In this article, the performance of three major components of soft computing viz., genetic algorithm, simulated annealing and differential evolution have been studied for developing fuzzy clustering of microarray gene expression data. microarray technology permits simultaneous monitoring of the expression levels of a large number of genes over different time points. Clustering is an important unsupervised analysis tool in this domain for finding groups of co-expressed genes. In this regard, the performance of the well-known fuzzy c-means algorithm is also studied in addition to the above mentioned soft computing-based approaches. Subsequently, support vector machine, a well-known technique for supervised learning, is utilized to improve the result of the clustering techniques. For this purpose, a fraction of the data points selected from different clusters based on their proximity to the respective centers, is used for training the support vector machine. The cluster assignments of the remaining points are thereafter determined using the trained classifier. Two publicly available benchmark microarray data sets have been used for demonstrating the effectiveness of the proposed approaches. Biological significance tests have been performed to show that the proposed method is able to produce biologically relevant clusters of co-expressed genes. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Biological significance test Differential evolution Fuzzy clustering Genetic algorithm microarray data Simulated annealing Soft computing

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Integrated analysis of the heterogeneous microarray data

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BMC BIOINFORMATICS 2011年第5期12卷 1-8页

作者： Yi, Sung Gon Park, Taesung Seoul Natl Univ Dept Stat Seoul South Korea Case Western Reserve Univ Dept Epidemiol & Biostat Cleveland OH 44106 USA

Background: As the magnitude of the experiment increases, it is common to combine various types of microarrays such as paired and non-paired microarrays from different laboratories or hospitals. Thus, it is important to analyze microarray data together to derive a combined conclusion after accounting for heterogeneity among data sets. One of the main objectives of the microarray experiment is to identify differentially expressed genes among the different experimental groups. We propose the linear mixed effect model for the integrated analysis of the heterogeneous microarray data sets. Results: The proposed linear mixed effect model was illustrated using the data from 133 microarrays collected at three different hospitals. Though simulation studies, we compared the proposed linear mixed effect model approach with the meta-analysis and the ANOVA model approaches. The linear mixed effect model approach was shown to provide higher powers than the other approaches. Conclusions: The linear mixed effect model has advantages of allowing for various types of covariance structures over ANOVA model. Further, it can handle easily the correlated microarray data such as paired microarray data and repeated microarray data from the same subject.

关键词： microarray data Covariance Structure ANOVA Model Linear Mixed Effect Model Statistical Test Procedure

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Hierarchical mixture models for biclustering in microarray data

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STATISTICAL MODELLING 2011年第6期11卷 489-505页

作者： Martella, F. Alfo, M. Vichi, M. Univ Roma La Sapienza Dipartimento Sci Stat I-00185 Rome Italy

In the last few years, model-based clustering techniques have become widely used in the context of microarray data analysis. In this empirical context, a potential purpose for statistical approaches is the identification of clusters of genes that are co-expressed under subsets of experimental conditions. We discuss a hierarchical mixture model to combine advantages of allowing for dependence within gene clusters and for simultaneous clustering of genes and experimental conditions. Thanks to the adopted hierarchical structure, we may distinguish gene clusters from mixture components, where the latter may represent intra-cluster gene-specific extra-Gaussian departures. To cluster experimental conditions, instead, we suggest a suitable parameterization of component-specific means by using a binary row stochastic matrix representing condition membership. The performance of the proposed approach is discussed on both simulated and real datasets.

关键词： Hierarchical mixture model biclustering microarray data

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Wavelet selection for disease classification by DNA microarray data

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第1期38卷 990-995页

作者： Nanni, Loris Lumini, Alessandra Univ Bologna DEIS IEIIT CNR I-40136 Bologna Italy

The microarrays report the measures of the expression levels of tens of thousands of genes, this high dimensional feature vector contains also irrelevant information for accurate classification. Moreover, only few training samples are available, hence for avoiding the curse of dimensionality problem a feature reduction should be performed before the classification step. Here, we proposed a set of orthogonal wavelet detail coefficients of different wavelet mothers to extract the features from the microarray data. We propose to use a multi-classifiers where each classifier, a support vector machine, is trained using a different set of detail coefficients, the classifiers are combined by "sum rule". The detail coefficients set selection is performed by running Sequential Forward Floating Selection (SFFS). The goodness of the proposed method is validated using the area under the ROC curve as performance indicator, the experiments are carried out on four-datasets: Breast dataset;Ovarian dataset;Lung dataset;Prostate dataset. The results show that the proposed method outperforms the performance that can be obtained by a single set of detail coefficients. Moreover, we have shown that, also using as features the detail coefficients, a random subspace of classifiers outperforms the stand-alone classifiers. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： microarray data 1-D wavelet transform Support vector machine Fusion of classifiers

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Regularized logistic regression without a penalty term: An application to cancer classification with microarray data

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第5期38卷 5110-5118页

作者： Bielza, Concha Robles, Victor Larranaga, Pedro Tech Univ Madrid Dept Artificial Intelligence Madrid Spain Tech Univ Madrid Dept Comp Architecture & Technol Madrid Spain

Regularized logistic regression is a useful classification method for problems with few samples and a huge number of variables. This regression needs to determine the regularization term, which amounts to searching for the optimal penalty parameter and the norm of the regression coefficient vector. This paper presents a new regularized logistic regression method based on the evolution of the regression coefficients using estimation of distribution algorithms. The main novelty is that it avoids the determination of the regularization term. The chosen simulation method of new coefficients at each step of the evolutionary process guarantees their shrinkage as an intrinsic regularization. Experimental results comparing the behavior of the proposed method with Lasso and ridge logistic regression in three cancer classification problems with microarray data are shown. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Logistic regression Regularization Estimation of distribution algorithms Cancer classification microarray data

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Sequential patterns mining and gene sequence visualization to discover novelty from microarray data

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JOURNAL OF BIOMEDICAL INFORMATICS 2011年第5期44卷 760-774页

作者： Sallaberry, A. Pecheur, N. Bringay, S. Roche, M. Teisseire, M. Irstea UMR TETIS Maison Teledetect F-34093 Montpellier France Univ Montpellier 3 MIAp Dept F-34199 Montpellier 5 France Univ Montpellier 2 CNRS LIRMM F-34095 Montpellier 5 France INRIA Bordeaux Sud Ouest LaBRI F-33405 Talence France

data mining allow users to discover novelty in huge amounts of data. Frequent pattern methods have proved to be efficient, but the extracted patterns are often too numerous and thus difficult to analyze by end users. In this paper, we focus on sequential pattern mining and propose a new visualization system to help end users analyze the extracted knowledge and to highlight novelty according to databases of referenced biological documents. Our system is based on three visualization techniques: clouds, solar systems, and treemaps. We show that these techniques are very helpful for identifying associations and hierarchical relationships between patterns among related documents. Sequential patterns extracted from gene data using our system were successfully evaluated by two biology laboratories working on Alzheimer's disease and cancer. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Visualization data mining Bioinformatics Sequential patterns microarray data Gene data

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Gene selection and sample classification on microarray data based on adaptive genetic algorithm/k-nearest neighbor method

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第5期38卷 4661-4667页

作者： Lee, Chien-Pang Lin, Wen-Shin Chen, Yuh-Min Kuo, Bo-Jein Natl Chung Hsing Univ Dept Agron Div Biometry Taichung 40227 Taiwan China Med Univ Sch Nursing Taichung 40402 Taiwan

Recently, microarray technology has widely used on the study of gene expression in cancer diagnosis. The main distinguishing feature of microarray technology is that can measure thousands of genes at the same time. In the past, researchers always used parametric statistical methods to find the significant genes. However, microarray data often cannot obey some of the assumptions of parametric statistical methods, or type I error may be over expanded. Therefore, our aim is to establish a gene selection method without assumption restriction to reduce the dimension of the data set. In our study, adaptive genetic algorithm/k-nearest neighbor (AGA/KNN) was used to evolve gene subsets. We find that AGA/KNN can reduce the dimension of the data set, and all test samples can be classified correctly. In addition, the accuracy of AGA/KNN is higher than that of GA/KNN, and it only takes half the CPU time of GA/KNN. After using the proposed method, biologists can identify the relevant genes efficiently from the sub-gene set and classify the test samples correctly. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Gene selection Sample classification Adaptive genetic algorithm k-Nearest neighbor microarray data

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Screening and identification of seed-specific genes using digital differential display tools combined with microarray data from common wheat

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BMC GENOMICS 2011年第1期12卷 1-12页

作者： Yang, Xinglu Xu, Hongliang Li, Wenhui Li, Le Sun, Jinyue Li, Yaxuan Yan, Yueming Hu, Yingkao Capital Normal Univ Coll Life Sci Beijing 100048 Peoples R China Natl Res Council Canada Inst Plant Biotechnol Saskatoon SK S7N 0W9 Canada

Background: Wheat is one of the most important cereal crops for human beings, with seeds being the tissue of highly economic value. Various morphogenetic and metabolic processes are exclusively associated with seed maturation. The goal of this study was to screen and identify genes specifically expressed in the developing seed of wheat with an integrative utilization of digital differential display (DDD) and available online microarray databases. Results: A total of 201 unigenes were identified as the results of DDD screening and microarray database searching. The expressions of 6 of these were shown to be seed-specific by qRT-PCR analysis. Further GO enrichment analysis indicated that seed-specific genes were mainly associated with defense response, response to stress, multi-organism process, pathogenesis, extracellular region, nutrient reservoir activity, enzyme inhibitor activity, antioxidant activity and oxidoreductase activity. A comparison of this set of genes with the rice (Oryza sativa) genome was also performed and approximately three-fifths of them have rice counterparts. Between the counterparts, around 63% showed similar expression patterns according to the microarray data. Conclusions: In conclusion, the DDD screening combined with microarray data analysis is an effective strategy for the identification of seed-specific expressed genes in wheat. These seed-specific genes screened during this study will provide valuable information for further studies about the functions of these genes in wheat.

关键词： Gene Ontology microarray data Homologous Gene Late Embryogenesis Abundant Late Embryogenesis Abundant Protein

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