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International Symposium on Communications and Information Technologies

作者： Phimmarin Keerin Werasak Kurutach Tossapon Boongoen Faculty of Information Science and Technology Mahanakorn University of Technology Bangkok Thailand Department of Mathematics and Computer Science Royal Thai Air Force Academy Bangkok Thailand

ISBN: (纸本)9781467355797

Gene expressions measured during a microarray experiment usually encounter the native problem of missing values. These are due to possible errors occurring in the primary experiments, image acquisition and interpretation processes. Leaving this unsolved may critically degrade the reliability of any consequent downstream analysis or medical application. Yet, a further study of microarray data may not be possible with many standard analysis methods that require a complete data set. This paper introduces a new method to impute missing values in microarray data. The proposed algorithm, CLLS impute, is an extension of local least squares imputation with local data clustering being incorporated for improved quality and efficiency. Gene expression data is typically represented as a matrix whose rows and columns corresponds to genes and experiments, respectively. CLLS kicks off by finding a complete dataset via the removal of rows with missing value(s). Then, gene clusters and their corresponding centroids are obtained by applying a clustering technique on the complete dataset. A set of similar genes of the target gene (with missing values) are those belonging to the cluster, whose centroid is the closest to the target. Having known this, the target gene is imputed by applying regression analysis with similar genes previously determined. Empirical evaluation with several published gene expression datasets suggest that the proposed technique performs better than the classical local least square method and recently developed techniques found in the literature.

关键词： missing value imputation microarray data clustering regression

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A modified F-test for hypothesis testing in large-scale data

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JOURNAL OF BIOPHARMACEUTICAL STATISTICS 2018年第6期28卷 1078-1089页

作者： Salehi, Mohsen Mohammadpour, Adel Mohammadi, Mohammad Aminghafari, Mina Amirkabir Univ Technol Fac Math & Comp Sci Dept Stat Tehran Iran Behbahan Khatam Alanbia Univ Technol Fac Basic Sci Dept Stat Behbahan Iran

Nowadays, performing simultaneous hypothesis testing under small sample sizes in large-scale features has received unprecedented attention in practice. Traditional approaches are not appropriate for this purpose. Instead, a novel approach which is based on the null statistic is used in the literature. The null statistic approach, using the permutation samples of all features, permits the more accurate estimation of the rho-value for a target feature. However, the known methods, working based on the null statistic, cannot be applied to features with three or more levels. In the present study, an attempt is made to introduce a new permutation test statistic which can be modified to the null statistic approach when features have three or more levels. The robustness of the proposed test statistic and its performance in estimating the rho-value are demonstrated through conducting simulation studies and the analysis of two real datasets.

关键词： microarray data null statistic one-way ANOVA permutation test

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Fold change based approach for identification of significant network markers in breast, lung and prostate cancer

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IET SYSTEMS BIOLOGY 2018年第5期12卷 213-218页

作者： Makhijani, Richa K. Raut, Shital A. Purohit, Hemant J. Visvesvaraya Natl Inst Technol Dept Comp Sci & Engn Nagpur 440010 MS India Natl Environm Engn Res Inst Environm Genom Div Nagpur 440020 MS India

Cancer belongs to a class of highly aggressive diseases and a leading cause of death in the world. With more than 100 types of cancers, breast, lung and prostate cancer remain to be the most common types. To identify essential network markers (NMs) and therapeutic targets in these cancers, the authors present a novel approach which uses gene expression data from microarray and RNA-seq platforms and utilises the results from this data to evaluate protein-protein interaction (PPI) network. Differentially expressed genes (DEGs) are extracted from microarray data using three different statistical methods in R, to produce a consistent set of genes. Also, DEGs are extracted from RNA-seq data for the same three cancer types. DEG sets found to be common in both platforms are obtained at three fold change (FC) cut-off levels to accurately identify the level of change in expression of these genes in all three cancers. A cancer network is built using PPI data characterising gene sets at log-FC (LFC)>1, LFC>1.5 and LFC>2, and interconnection between principal hub nodes of these networks is observed. Resulting network of hubs at three FC levels highlights prime NMs with high confidence in multiple cancers as validated by Gene Ontology functional enrichment and maximal complete subgraphs from CFinder.

关键词： cancer proteins RNA bioinformatics statistical analysis genetics molecular biophysics ontologies (artificial intelligence) lung cancer network PPI data gene sets multiple cancers Gene Ontology functional enrichment prostate cancer gene expression data RNA-seq platforms protein-protein interaction network DEG microarray data RNA-seq data cancer types lung cancer diseases breast cancer network markers differentially expressed genes fold change based approach CFinder statistical methods

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A Review of Software Tools for Pathway Crosstalk Inference

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CURRENT BIOINFORMATICS 2018年第1期13卷 64-72页

作者： Dussaut, Julieta S. Cecchini, Rocio L. Gallo, Cristian A. Ponzoni, Ignacio Carballido, Jessica A. Univ Nacl Sur CONICET ICIC San Andres 800-Campus Palihue RA-8000 Bahia Blanca Buenos Aires Argentina

Background: We are living in an era that is in general characterized by a lot of data but little information. An enormous amount of biological data collected over several years is now presented as annotations and databases. In this context, all this data properly combined and grouped has great potential for enabling novel discoveries which would then, finally and hopefully, lead to advances in biology and medicine. The inference of different kinds of relations between pathways constitutes a challenging step towards the analysis of all these sources of biological data. Objective: This review article aims at outlining several methods that analyze associations between pathways starting from different sources of information, namely the internet, databases, and/or gene expression data. Methods: The article consists of a summary of the most important methods for pathway networks inference and arranges them according to the data they use as well as the findings they provide. Results: The advantages and drawbacks of each considered methodology are presented, as well as a taxonomy tree and summary table as an overview of the discussion. Conclusion: The methods explained in this paper consist especially of those that explore the concept of associations between pathways using microarray experimental data and/or topological or curated information. Each strategy was introduced, classified and analyzed. The identification of different kinds of associations between pathways plays a central role in systems biology, revealing information which is undetectable at a gene level. Therefore, a comprehensible understanding of the benefits and limitations of these approaches could be the key to the development of new computational strategies for genome-wide analysis.

关键词： Relations between pathways topological pathway information microarray data pathway enrichment inference methods

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HYBRIDIZATION OF GENETIC AND QUANTUM ALGORITHM FOR GENE SELECTION AND CLASSIFICATION OF microarray data

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INTERNATIONAL JOURNAL OF FOUNDATIONS OF COMPUTER SCIENCE 2012年第2期23卷 431-444页

作者： Abderrahim, Allani Talbi, El-Ghazali Khaled, Mellouli Inst Super Gest Cite Bouchoucha 2000 Bardo Tunisia LIFL INRIA Futurs F-59655 Villeneuve Dascq France King Saud Univ Riyadh Saudi Arabia Inst Hautes Etud Commerciales Carthage Carthage Presidence Cart Tunisia

In this work, we hybridize the Genetic Quantum Algorithm with the Support Vector Machines classifier for gene selection and classification of high dimensional microarray data. We named our algorithm GQA(SVM). Its purpose is to identify a small subset of genes that could be used to separate two classes of samples with high accuracy. A comparison of the approach with different methods of literature, in particular GA(SVM) and PSOSVM [2], was realized on six different datasets issued of microarray experiments dealing with cancer (leukemia, breast, colon, ovarian, prostate, and lung) and available on Web. The experiments clearified the very good performances of the method. The first contribution shows that the algorithm GQA(SVM) is able to find genes of interest and improve the classification On a meaningful way. The second important contribution consists in the actual discovery of new and challenging results on datasets used.

关键词： Genetic quantum algorithm quantum computing classification support vector machines feature selection gene selection microarray data

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A novel effective diagnosis model based on optimized least squares support machine for gene microarray

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APPLIED SOFT COMPUTING 2018年 66卷 50-59页

作者： Gao, Xinteng Liu, Xinggao Zhejiang Univ Coll Control Sci & Engn Natl Engn Res Ctr Ind Automat Hangzhou 310027 Zhejiang Peoples R China

Classification for cancer diagnosis is a significant topic in bioinformatics. microarray data generates a big challenge for the diagnosis prediction due to tens of thousands of genes but dozens of samples. The feature dimension needs to be reduced for better diagnosis prediction. Moreover, the parameters of classifiers required to be optimized have a significant impact on the classifiers' performance. This paper presents a novel effective approach for diagnosis prediction. First, the normalization procedure for the raw data is carried out. Then, considering both the complexity of dimension of the microarray data and the specific classifier, support vector machine method based on recursive feature elimination (SVMRFE) is applied for selecting optimal gene subset. Finally, a hybrid method based on fruit fly optimization and particle swarm optimization (PSOFOA) is proposed to optimizing the classifier (LSSVM) parameters. An integrated work of disease diagnosis is obtained. By this means, the accuracy of 100% is achieved with only 4 features (genes), which is the best result compared to all published papers. The proposed method is also compared with some specific methods The result illustrates that the proposed method achieves a best prediction performance. (C) 2018 Elsevier B.V. All rights reserved.

关键词： microarray data SVMRFE PSOFOA LSSVM Disease diagnosis

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Random Forest for Gene Selection and microarray data Classification

Random Forest for Gene Selection and Microarray Data Classif...

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3rd Conference of Knowledge Technology Week (KTW 2011)

作者： Moorthy, Kohbalan Mohamad, Mohd Saberi Univ Teknol Malaysia Artificial Intelligence & Bioinformat Res Grp Fac Comp Sci & Informat Syst Skudai 81310 Johor Malaysia

ISBN: (纸本)9783642328251

A random forest method has been selected to perform both gene selection and classification of the microarray data. The goal of this research is to develop and improve the random forest gene selection method. Hence, improved gene selection method using random forest has been proposed to obtain the smallest subset of genes as well as biggest subset of genes prior to classification. In this research, ten datasets that consists of different classes are used, which are Adenocarcinoma, Brain, Breast (Class 2 and 3), Colon, Leukemia, Lymphoma. NCI60, Prostate and Small Round Blue-Cell Tumor (SRBCT). Enhanced random forest gene selection has performed better in terms of selecting the smallest subset as well as biggest subset of informative genes through gene selection. Furthermore, the classification performed on the selected subset of genes using random forest has lead to lower prediction error rates compared to existing method and other similar available methods.

关键词： Random forest gene selection classification microarray data cancer classification gene expression data

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Parallel Hybrid Metaheuristic for Multi-objective Biclustering in microarray data

Parallel Hybrid Metaheuristic for Multi-objective Biclusteri...

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26th IEEE International Parallel and Distributed Processing Symposium (IPDPS) / Workshop on High Performance data Intensive Computing

作者： Seridi, Khedidja Jourdan, Laetitia Talbi, El-Ghazali CNRS LIFL INRIA Lille Nord Europe F-59650 Villeneuve Dascq France

ISBN: (纸本)9780769546766

To deeper examine the gene expression data, a new data mining task is more more used: the biclustering. Biclustering consists in extracting genes that behave similarly under some experimental conditions. As the Biclustering problem is NP-Complete in most of its variants, many heuristics and metaheuristics have been deisgned to solve it. Proposed algorithms in literature allow the extraction of interesting biclusters but are often time consuming. In this work, we propose a new parallel hybrid multi-objective metaheuristic based on the well known multi objective metaheuristic NSGA-II (Non-dominated Sorting Genetic Algorithm II), CC (Cheng and Church) heuristic and a multi-objective local search, PLS-1 (Pareto Local Search I). Experimental results on real data sets show that our approach can find significant biclusters of high quality. The speed-up of our algorithm is important with regard to the sequential version.

关键词： Biclustering Multi-objective optimization Parallel metaheuristic microarray data

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A Novel Hybrid Feature Selection Model for Classification of Neuromuscular Dystrophies Using Bhattacharyya Coefficient, Genetic Algorithm and Radial Basis Function Based Support Vector Machine

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INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES 2018年第2期10卷 244-250页

作者： Anand, Divya Pandey, Babita Pandey, Devendra K. Lovely Profess Univ Sch Comp Sci & Engn Chaheru Punjab India Lovely Profess Univ Sch Comp Applicat Chaheru Punjab India Lovely Profess Univ Sch Biosci Chaheru Punjab India

An accurate classification of neuromuscular disorders is important in providing proper treatment facilities to the patients. Recently, the microarray technology is employed to monitor the level of activity or expression of large number of genes simultaneously. The gene expression data derived from the microarray experiment usually involve a large number of genes but a very few number of samples. There is a need to reduce the dimension of gene expression data which intends to find a small set of discriminative genes that accurately classifies the samples of various kinds of diseases. So, our goal is to find a small subset of genes which ensures the accurate classification of neuromuscular disorders. In the present paper, we propose a novel hybrid feature selection model for classification of neuromuscular disorders. The process of feature selection is done in two phases by integrating Bhattacharyya coefficient and genetic algorithm (GA). In the first phase, we find Bhattacharyya coefficient to choose a candidate gene subset by removing the most redundant genes. In the second phase, the target gene subset is created by selecting the most discriminative gene subset by applying GA wherein the fitness function is calculated using radial basis function support vector machine (RBF SVM). The proposed hybrid algorithm is applied on two publicly available microarray neuromuscular disorders datasets. The results are compared with two individual techniques of feature selection, namely Bhattacharyya coefficient and GA, and one integrated technique, i.e., Bhattacharyya-GA wherein the fitness function of GA is calculated using four other classifiers, which shows that the proposed integrated method is capable of giving the better classification accuracy.

关键词： Bhattacharyya coefficient Genetic algorithm Support vector machine Neuromuscular disorders microarray data Radial basis function

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Clustering microarray data using Fuzzy Clustering with Viewpoints

Clustering Microarray Data using Fuzzy Clustering with Viewp...

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12th IEEE International Conference on BioInformatics and BioEngineering (BIBE)

作者： Karayianni, Katerina N. Spyrou, George M. Nikita, Konstantina S. Natl Tech Univ Athens Sch Elect & Comp Engn Athens Greece Acad Athens Biomed Res Fdn Athens Greece

ISBN: (纸本)9781467343589;9781467343572

This paper studies the application of fuzzy clustering with viewpoints in order to cluster cell samples according to their gene expression profile. This method combines fuzzy clustering with external domain knowledge represented by the so-called viewpoints. The viewpoints that we employ are obtained from previously available expression data. The method was compared to the clustering algorithms of k-means, fuzzy c-means, affinity propagation, as well as a method of clustering microarray data that is based on prior biological knowledge, and has shown comparable/improved results over them.

关键词： Clustering microarray data prior knowledge viewpoints

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