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European Modelling Symposium

作者： Marion Adebiyi Josiah Oghuan Segun Fatumo Ezekiel Adebiyi Jason Rasgon Department of Computer and Information Sciences Covenant University Department of Entomology The Center for Infectious Disease Dynamics and the Huck Institutes of the Life Sciences The Pennsylvania State University

ISBN: (纸本)9781479925797

Insecticide resistance, a character inherited that encompasses alteration in one or more of insect's genes is now a major public health challenge combating world efforts on malaria control strategies. Anopheles has developed heavy resistance to pyrethroids, the only World Health Organization (WHO) recommended class for Indoor Residual Spray (IRS) and Long-Lasting Insecticide Treated Nets (LLITNs) through P450 pathways. We used the biochemical network of Anopheles gambiae (henceforth Ag) to deduce its resistance mechanism(s) using two expression data (when Ag is treated with pyrethroid and when controlled). The employed computational techniques are accessible by a robust, multi-faceted and friendly automated graphic user interface (GUI) tagged 'workbench' with JavaFX Scenebuilder. In this work, we introduced a computational platform to determine and also elucidate for the first time resistance mechanism to a commonly used class of insecticide, Pyrethroid. Significantly, our work is the first computational work to identify genes associated or involved in the efflux system in Ag and as a resistance mechanism in the Anopheles.

关键词： Anopheles gambiae Biochemical network microarray data Resistance mechanism Features extraction

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Two Extensions to Multi-label Correlation-Based Feature Selection: a case study in bioinformatics

Two Extensions to Multi-label Correlation-Based Feature Sele...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Suwimol Jungjit M. Michaelis Alex A. Freitas J. Cinatl School of Computing University of Kent Canterbury CT2 7NF UK School of Biosciences University of Kent Canterbury CT2 7NJ UK Institut fuer Medizinische Virologie Klinikum der Goethe-Universitaet Paul Ehrlich-Str. 40 60596 Frankfurt am Main Germany

ISBN: (纸本)9781479906505

This paper proposes two extensions to a Multi-Label Correlation Based Feature Selection Method (ML-CFS): (1) ML-CFS using the absolute value of the correlation coefficient in the equation for evaluating a candidate feature subset, and (2) ML-CFS using Mutual Information for class label weighting. These extensions are evaluated in a bioinformatics case study addressing the multi-label classification of a cancer-related DNA microarray dataset with over 20,000 features. The results show that ML-CFS with absolute value of correlation obtained a significantly better predictive accuracy (smaller hamming loss) than the original ML-CFS. On the other hand, using Mutual Information to assign weights to labels showed some positive effect when using the ML-RBF classifier, but it showed a negative effect when using the ML-kNN classifier.

关键词： multi-label feature selection multi-label classification microarray data Feature extraction Bioinformatics Absolute value correlation coefficient (r) Case studies Classifiers

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Using partially ordered sets to represent and predict true patterns of gene response to treatments

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BMC BIOINFORMATICS 2013年第17-Sup期14卷 A20-A20页

作者： Vo, Nam S. Vinhthuy Phan Univ Memphis Dept Comp Sci Memphis TN 38152 USA

Doc number: A20

关键词： Pairwise Comparison Expression data Large Sample Size microarray data Gene Expression Level

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Active learning for microarray data

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2008年第1期47卷 85-96页

作者： Vogiatzis, D. Tsapatsoulis, N. Univ Cyprus Dept Comp Sci CY-1678 Nicosia Cyprus Univ Peloponnese Dept Telecommun Sci & Technol Tripoli 22100 Greece

In supervised learning it is assumed that it is straightforward to obtain labeled data. However, in reality labeled data can be scarce or expensive to obtain. Active learning (AL) is a way to deal with the above problem by asking for the labels of the most "informative" data points. We propose an AL method based on a metric of classification confidence computed on a feature subset of the original feature space which pertains especially to the large number of dimensions (i.e. examined genes) of microarray experiments. DNA microarray expression experiments permit the systematic study of the correlation of the expression of thousands of genes. Feature selection is critical in the algorithm because it enables faster and more robust retraining of the classifier. The approach that is followed for feature selection is a combination of a variance measure and a genetic algorithm. We have applied the proposed method on DNA microarray data sets with encouraging results. In particular we studied data sets concerning: small round blue cell tumours (four types), Leukemia (two types), lung cancer (two types) and prostate cancer (healthy, unhealthy) (c) 2007 Elsevier Inc. All rights reserved.

关键词： active learning microarray data classification confidence feature selection genetic algorithms

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Investigation of self-organizing oscillator networks for use in clustering microarray data

IEEE TRANSACTIONS ON NANOBIOSCIENCE

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IEEE TRANSACTIONS ON NANOBIOSCIENCE 2008年第1期7卷 65-79页

作者： Salem, Sameh A. Jack, Lindsay B. Nandi, Asoke K. Univ Liverpool Dept Elect Engn & Elect Signal Proc & Commun Grp Liverpool L69 3GJ Merseyside England

The self-organizing oscillator network (SOON) is a comparatively new clustering algorithm that does not require the knowledge of the number of clusters. The SOON is distance based, and its clustering behavior is different to density-based algorithms in a number of ways. This paper examines the effect of adjusting the control parameters of the SOON with four different datasets;the first is a (communications) modulation dataset representing one modulation scheme under a variety of noise conditions. This allows the assessment of the behavior of the algorithm with data varying between highly separable and nonseparable cases. The main thrust of this paper is to evaluate its efficacy in biological datasets. The second is taken from microarray experiments on the cell cycle of yeast, while the third and the fourth represent two microarray cancer datasets, i.e., the lymphoma and the liver cancer datasets. The paper demonstrates that the SOON is a viable tool to analyze these problems, and can add many useful insights to the biological data that may not always be available using other clustering methods.

关键词： cluster validation data clustering gene expression microarray data unsupervised classification

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Using Generalized Procrustes Analysis (GPA) for normalization of cDNA microarray data

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BMC BIOINFORMATICS 2008年第1期9卷 1-13页

作者： Xiong, Huiling Zhang, Dapeng Martyniuk, Christopher J. Trudeau, Vance L. Xia, Xuhua Univ Ottawa Dept Biol CAREG Ottawa ON K1N 6N5 Canada

Background: Normalization is essential in dual-labelled microarray data analysis to remove non-biological variations and systematic biases. Many normalization methods have been used to remove such biases within slides ( Global, Lowess) and across slides ( Scale, Quantile and VSN). However, all these popular approaches have critical assumptions about data distribution, which is often not valid in practice. Results: In this study, we propose a novel assumption-free normalization method based on the Generalized Procrustes Analysis ( GPA) algorithm. Using experimental and simulated normal microarray data and boutique array data, we systemically evaluate the ability of the GPA method in normalization compared with six other popular normalization methods including Global, Lowess, Scale, Quantile, VSN, and one boutique array-specific housekeeping gene method. The assessment of these methods is based on three different empirical criteria: across-slide variability, the Kolmogorov-Smirnov ( K-S) statistic and the mean square error ( MSE). Compared with other methods, the GPA method performs effectively and consistently better in reducing across-slide variability and removing systematic bias. Conclusion: The GPA method is an effective normalization approach for microarray data analysis. In particular, it is free from the statistical and biological assumptions inherent in other normalization methods that are often difficult to validate. Therefore, the GPA method has a major advantage in that it can be applied to diverse types of array sets, especially to the boutique array where the majority of genes may be differentially expressed.

关键词： Mean Square Error microarray data Normalization Method Reference Array Generalize Procrustes Analysis

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Constructing the gene regulation-level representation of microarray data for cancer classification

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JOURNAL OF BIOMEDICAL INFORMATICS 2008年第1期41卷 95-105页

作者： Wong, Hau-San Wang, Hong-Qiang City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China

In this paper, we propose a regulation-level representation for microarray data and optimize it using genetic algorithms (GAs) for cancer classification. Compared with the traditional expression-level features, this representation can greatly reduce the dimensionality of microarray data and accommodate noise and variability such that many statistical machine-learning methods now become applicable and efficient for cancer classification. Experimental results on real-world microarray datasets show that the regulation-level representation can consistently converge at a solution with three regulation levels. This verifies the existence of the three regulation levels (up-regulation, down-regulation and non-significant regulation) associated with a particular biological phenotype. The ternary regulation-level representation not only improves the cancer classification capability but also facilitates the visualization of microarray data. (C) 2007 Elsevier Inc. All rights reserved.

关键词： cancer classification genetic algorithms gene expression levels gene regulation levels histogram microarray data

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COMBAT GA-BASED GENE SELECTION FOR CLASSIFICATION OF microarray data

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BIOMEDICAL ENGINEERING-APPLICATIONS BASIS COMMUNICATIONS 2008年第6期20卷 345-352页

作者： Chuang, Li-Yeh Yang, Cheng-San Li, Jung-Chike Yang, Cheng-Hong Natl Kaohsiung Univ Appl Sci Dept Elect Engn Kaohsiung 80708 Taiwan I Shou Univ Dept Chem Engn Kaohsiung 80041 Taiwan Natl Cheng Kung Univ Inst Biomed Engn Tainan 70101 Taiwan

microarray data can provide valuable results for a variety of gene expression profile problems and contribute to advances in clinical medicine. The application of microarray data on cancer-type classification has recently gained in popularity. The properties of microarray data contain a large number of features ( genes) with high dimensions, and one in the multi-class category. These facts make testing and training of general classification methods difficult. Reducing the number of genes and achieving lower classification error rates are the main issues to be solved. The classification of microarray data samples can be regarded as a feature selection and classifier design problem. The goal of feature selection is to select those subsets of differentially expressed genes that are potentially relevant for distinguishing the sample classes. Classical genetic algorithms (GAs) may suffer from premature convergence and thus lead to poor experimental results. In this paper, combat genetic algorithm (CGA) is used to implement the feature selection, and a K-nearest neighbor with the leave-one-out cross-validation method serves as a classifier of the CGA fitness function for the classification problem. The proposed method was applied to 10 microarray data sets that were obtained from the literature. The experimental results show that the proposed method not only effectively reduced the number of gene expression levels but also achieved lower classification error rates.

关键词： Feature selection microarray data combat genetic algorithm K-nearest neighbor leave-one-out cross-validation

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GEPAS, a web-based tool for microarray data analysis and interpretation

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NUCLEIC ACIDS RESEARCH 2008年第Web Server issue期36卷 W308-W314页

作者： Tarraga, Joaquin Medina, Ignacio Carbonell, Jose Huerta-Cepas, Jaime Minguez, Pablo Alloza, Eva Al-Shahrour, Fatima Vegas-Azcarate, Susana Goetz, Stefan Escobar, Pablo Garcia-Garcia, Francisco Conesa, Ana Montaner, David Dopazo, Joaquin CIPF Bioinformat Dept E-46013 Valencia Spain CIPF INB Funct Genom Node E-46013 Valencia Spain CIPF Ctr Biomed Res Rare Dis CIBERER E-46013 Valencia Spain Univ Carlos III Madrid Dept Stat E-28903 Getafe Spain

Gene Expression Profile Analysis Suite (GEPAS) is one of the most complete and extensively used web-based packages for microarray data analysis. During its more than 5 years of activity it has continuously been updated to keep pace with the state-of-the-art in the changing microarray data analysis arena. GEPAS offers diverse analysis options that include well established as well as novel algorithms for normalization, gene selection, class prediction, clustering and functional profiling of the experiment. New options for time-course (or dose-response) experiments, microarray-based class prediction, new clustering methods and new tests for differential expression have been included. The new pipeliner module allows automating the execution of sequential analysis steps by means of a simple but powerful graphic interface. An extensive re-engineering of GEPAS has been carried out which includes the use of web services and Web 2.0 technology features, a new user interface with persistent sessions and a new extended database of gene identifiers. GEPAS is nowadays the most quoted web tool in its field and it is extensively used by researchers of many countries and its records indicate an average usage rate of 500 experiments per day. GEPAS, is available at http://***.

关键词： .clustering module microarray data normalization class prediction web services GEPAS pipeliner

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An integrated algorithm for gene selection and classification applied to microarray data of ovarian cancer

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2008年第1期42卷 81-93页

作者： Lee, Zne-Jung Huafan Univ Dept Informat Management Shihding Township 22301 Taipei County Taiwan

Objective: The type of data in microarray provides unprecedented amount of data. A typical microarray data of ovarian cancer consists of the expressions of tens of thousands of genes on a genomic scale, and there is no systematic procedure to analyze this information instantaneously. To avoid higher computational complexity, it needs to select the most Likely differentially expressed gene markers to explain the effects of ovarian cancer. Traditionally, gene markers are selected by ranking genes according to statistics or machine learning algorithms. In this paper, an integrated algorithm is derived for gene selection and classification in microarray data of ovarian cancer. Methods: First, regression analysis is applied to find target genes. Genetic algorithm (GA), particle swarm optimization (PSO), support vector machine (SVM), and analysis of variance (ANOVA) are hybridized to select gene markers from target genes. Finally, the improved fuzzy model is applied to classify cancer tissues. Results: The microarray data of ovarian cancer, obtained from China Medical University Hospital, is used to test the performance of the proposed algorithm. In simulation, 200 target genes are obtained after regression analysis and six gene markers are selected from the hybrid process of GA, PCO, SVM and ANOVA. Additionally, these gene markers are used to classify cancer tissues. Conclusions: The proposed algorithm can be used to analyze gene expressions and has superior performance in microarray data of ovarian cancer, and it can be performed on other studies for cancer diagnosis. (C) 2007 Elsevier B.V. All rights reserved.

关键词： ovarian cancer microarray data gene selection support vector machine genetic algorithm particle swarm optimization

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