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MiMiR: a comprehensive solution for storage, annotation and exchange of microarray data

BMC BIOINFORMATICS 2005年第1期6卷 1-10页

作者： Navarange, M Game, L Fowler, D Wadekar, V Banks, H Cooley, N Rahman, F Hinshelwood, J Broderick, P Causton, HC Univ London Imperial Coll Sci Technol & Med CSC IC Microarray Ctr London W12 0NN England

Background: The generation of large amounts of microarray data presents challenges for data collection, annotation, exchange and analysis. Although there are now widely accepted formats, minimum standards for data content and ontologies for microarray data, only a few groups are using them together to build and populate large-scale databases. Structured environments for data management are crucial for making full use of these data. Description: The MiMiR database provides a comprehensive infrastructure for microarray data annotation, storage and exchange and is based on the MAGE format. MiMiR is MIAME-supportive, customised for use with data generated on the Affymetrix platform and includes a tool for data annotation using ontologies. Detailed information on the experiment, methods, reagents and signal intensity data can be captured in a systematic format. Reports screens permit the user to query the database, to view annotation on individual experiments and provide summary statistics. MiMiR has tools for automatic upload of the data from the microarray scanner and export to databases using MAGE-ML. Conclusion: MiMiR facilitates microarray data management, annotation and exchange, in line with international guidelines. The database is valuable for underpinning research activities and promotes a systematic approach to data handling. Copies of MiMiR are freely available to academic groups under licence.

关键词： microarray data data Annotation microarray Gene Expression data Gene Expression database data Upload

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Random walk biclustering for microarray data

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INFORMATION SCIENCES 2008年第6期178卷 1479-1497页

作者： Angiulli, Fabrizio Cesario, Eugenio Pizzuti, Clara CNR ICAR I-87036 Arcavacata Di Rende CS Italy

A biclustering algorithm, based on a greedy technique and enriched with a local search strategy to escape poor local minima, is proposed. The algorithm starts with an initial random solution and searches for a locally optimal solution by successive transformations that improve a gain function. The gain function combines the mean squared residue, the row variance, and the size of the bicluster. Different strategies to escape local minima are introduced and compared. Experimental results on several microarray data sets show that the method is able to find significant biclusters, also from a biological point of view. (c) 2007 Elsevier Inc. All rights reserved.

关键词： biclustering microarray data local search

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Expectation Propagation for microarray data classification

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PATTERN RECOGNITION LETTERS 2010年第12期31卷 1618-1626页

作者： Hernandez-Lobato, Daniel Hernandez-Lobato, Jose Miguel Suarez, Alberto Catholic Univ Louvain Dept Comp Sci & Engn B-1348 Louvain Belgium Univ Autonoma Madrid Escuela Politecn Super E-28049 Madrid Spain

microarray experiments are a very promising tool for early diagnosis and disease treatment. The datasets obtained in these experiments typically consist of a small number of instances and a large number of covariates, most of which are irrelevant for discrimination. These characteristics pose severe difficulties for standard learning algorithms. A Bayesian approach can be useful to overcome these problems and produce more accurate and robust predictions. However, exact Bayesian inference is computationally costly and in many cases infeasible. In practice, some form of approximation has to be made. In this paper we consider a Bayesian linear model for microarray data classification based on a prior distribution that favors sparsity in the model coefficients. Expectation Propagation (EP) is then used to perform approximate inference as an alternative to computationally more expensive methods, such as Markov Chain Monte Carlo (MCMC) sampling. The model considered is evaluated on 15 microarray datasets and compared with other state-of-the-art classification algorithms. These experiments show that the Bayesian model trained with EP performs well on the datasets investigated and is also useful to identify relevant genes for subsequent analysis. (C) 2010 Elsevier B.V. All rights reserved.

关键词： microarray data Bayesian inference Expectation Propagation

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Sequential local least squares imputation estimating missing value of microarray data

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COMPUTERS IN BIOLOGY AND MEDICINE 2008年第10期38卷 1112-1120页

作者： Zhang, Xiaobai Song, Xiaofeng Wang, Huinan Zhan, Huanping Nanjing Univ Aeronaut & Astronaut Dept Biomed Engn Nanjing 210016 Peoples R China

Missing values in microarray data can significantly affect subsequent analysis, thus it is important to estimate these missing values accurately. In this paper, a sequential local least squares imputation (SLLSimpute) method is proposed to solve this problem. It estimates missing values sequentially from the gene containing the fewest missing values and partially utilizes these estimated values. In addition, an automatic parameter selection algorithm, which can generate an appropriate number of neighboring genes for each target gene, is presented for parameter estimation. Experimental results confirmed that SLLSimpute method exhibited better estimation ability compared with other currently used imputation methods. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Missing value estimation Imputation method Least squares principle Normalized root mean squared error (NRMSE) microarray data

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Statistical Analysis of microarray data Clustering using NMF, Spectral Clustering, Kmeans, and GMM

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022年第2期19卷 1173-1192页

作者： Mirzal, Andri King Fahd Univ Petr & Minerals Informat & Comp Sci Dept Dhahran 31261 Saudi Arabia

In unsupervised learning literature, the study of clustering using microarray gene expression datasets has been extensively conducted with nonnegative matrix factorization (NMF), spectral clustering, kmeans, and gaussian mixture model (GMM)are some of the most used methods. However, there is still a limited number of works that utilize statistical analysis to measure the significances of performance differences between these methods. In this paper, statistical analysis of performance differences between ten NMF, six spectral clustering, four GMM, and the standard kmeans algorithms in clustering eleven publicly available microarray gene expression datasets with the number of clusters ranges from two to ten is presented. The experimental results show that statistically NMFs and kmeans have similar performances and outperform spectral clustering. As spectral clustering can be used to uncover hidden manifold structures, the underperformance of spectral methods leads us to question whether the datasets have manifold structures. Visual inspection using multidimensional scaling plots indicates that such structures do not exist. Moreover, as the plots indicate that clusters in some datasets have elliptical boundaries, GMM methods are also utilized. The experimental results show that GMM methods outperform the other methods to some degree, and thus imply that the datasets follow gaussian distributions.

关键词： Clustering algorithms data models Matrix decomposition Task analysis Statistical analysis Standards Clustering methods Clustering gmm kmeans microarray data NMF spectral method statistical analysis

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Hybridization of Moth flame optimization algorithm and quantum computing for gene selection in microarray data

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第2期12卷 2731-2750页

作者： Dabba, Ali Tari, Abdelkamel Meftali, Samy Mohamed Boudiaf Univ Fac Math & Comp Sci Comp Sci Dept Msila Algeria Abderrahmane Mira Univ Fac Sci Comp Sci Dept Bejaia Algeria Med Comp Lab LIMED Bejaia Algeria Univ Lille Lille France Res Ctr Comp Sci Signal & Automat Control Lille C Lille France

Ever-increasing data in various fields like Bioinformatics field, which has led to the need to find a way to reduce the data dimensionality. Gene selection problem has a large number of genes (relevant, redundant or noise), which needs an effective method to help us in detecting diseases and cancer. In this problem, computational complexity is reduced by selecting a small number of genes, but it is necessary to choose the relevant genes to keep a high level of accuracy. Therefore, in order to find the optimal gene subset, it is essential to devise an effective exploration approach that can investigate a large number of possible gene subsets. In addition, it is required to use a powerful evaluation method to evaluate the relevance of these gene subsets. In this paper, we present a novel swarm intelligence algorithm for gene selection called quantum moth flame optimization algorithm (QMFOA), which based on hybridization between quantum computation and moth flame optimization (MFO) algorithm. The purpose of QMFOA is to identify a small gene subset that can be used to classify samples with high accuracy. The QMFOA has a simple two-phase approach, the first phase is a pre-processing that uses to address the difficulty of high-dimensional data, which measure the redundancy and the relevance of the gene, in order to obtain the relevant gene set. The second phase is a hybridization among MFOA, quantum computing, and support vector machine with leave-one-out cross-validation, etc., in order to solve the gene selection problem. We use quantum computing to guarantee a good trade-off between the exploration and the exploitation of the search space, while a new update moth operation using Hamming distance and Archimedes spiral allows an efficient exploration of all possible gene-subsets. The main objective of the second phase is to determine the best relevant gene subset of all genes obtained in the first phase. In order to assess the performance of the proposed QMFOA, we test Q

关键词： Gene expression Feature selection Moth flame optimization algorithm Quantum computing microarray data Cancer classification Bio-inspired algorithms Molecular biology Optimization algorithms Evolutionary algorithms Swarm intelligence

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A sequential feature extraction approach for naive bayes classification of microarray data

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EXPERT SYSTEMS WITH APPLICATIONS 2009年第6期36卷 9919-9923页

作者： Fan, Liwei Poh, Kim-Leng Zhou, Peng Natl Univ Singapore Dept Ind & Syst Engn Singapore 119260 Singapore Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing 210016 Peoples R China

Accurate classification of microarray data plays a vital role in cancer prediction and diagnosis. Previous Studies have demonstrated the usefulness of naive Bayes classifier in solving various classification problems. In microarray data analysis, however, the conditional independence assumption embedded in the classifier itself and the characteristics of microarray data, e.g. the extremely high dimensionality, may severely affect the classification performance of naive Bayes classifier. This paper presents a sequential feature extraction approach for naive Bayes classification of microarray data. The proposed approach consists of feature selection by stepwise regression and feature transformation by class-conditional independent component analysis. Experimental results on five microarray datasets demonstrate the effectiveness of the proposed approach in improving the performance of naive Bayes classifier in microarray data analysis. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： microarray data Naive Bayes Feature extraction Independent component analysis (ICA) Stepwise regression

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BIOLOGICAL ANALYSIS OF microarray data USING ORTHOGONAL FORWARD SELECTION WITH A CLUSTERING APPROACH

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JOURNAL OF BIOLOGICAL SYSTEMS 2015年第2期23卷 275-288页

作者： Kah, Wong Sou Moorthy, Kohbalan Mohamad, Mohd Saberi Kasim, Shahreen Deris, Safaai Omatu, Sigeru Yoshioka, Michifumi Univ Teknol Malaysia Fac Comp Artificial Intelligence & Bioinformat Res Grp Skudai 81310 Johor Malaysia Univ Tun Hussein Onn Malaysia Fac Informat Technol & Multimedia Dept Informat Syst Batu Pahat 86400 Malaysia Osaka Inst Technol Dept Elect Informat & Commun Engn Osaka 5358585 Japan Osaka Prefecture Univ Fac Engn Naka Ku Sakai Osaka 5998531 Japan

DNA microarray technology allows researchers to monitor the expression level of thousands of genes under various conditions in microarray experiments. However, high-dimensional data in microarray is a major challenge as the irrelevant genes often reduce the detection capability and increase the computation time. Many learning algorithms are not specifically developed to deal with the noisy genes, thus, incorporating them with gene selection techniques has become a necessity. In this paper, we propose a combined method of Gram-Schmidt orthogonal forward selection (OFS) and FunCluster to search for putatively co-regulated biological processes that share the co-expressed genes. There were two datasets used in this research: human white adipose tissue and human skeletal muscle. This study aimed to find a small subset of strongly correlated genes from the raw datasets to maximize the detection capability of cluster analysis. This method was found able to detect the clusters of biological categories that were overlooked in the previous research. Some clusters represented minor functions of the datasets and indicated more specific biological processes. Further, the computation time for both datasets was reduced using this proposed method, as the Gram-Schmidt OFS significantly reduced the dimensionality of the datasets.

关键词： microarray data Functional Profiling Orthogonal Forward Selection FunCluster

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Optimal significance analysis of microarray data in a class of tests whose null statistic can be constructed

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TEST 2012年第2期21卷 280-300页

作者： Fujisawa, Hironori Sakaguchi, Takayuki Inst Stat Math Tachikawa Tokyo 1908562 Japan Oita Univ Nursing & Hlth Sci Oita 8701201 Japan

microarray data often consist of a large number of genes and a small number of replicates. We have examined testing the null hypothesis of equality of mean for detecting differentially expressed genes. The p-value for each gene is often estimated using permutation samples not only for the target gene but also for other genes. This method has been widely used and discussed. However, direct use of the permutation method for the p-value estimation may not work well, because two types of genes are mixed in the sample;some genes are differentially expressed, whereas others are not. To overcome this difficulty, various methods for appropriately generating null permutation samples have been proposed. In this paper, we consider two classes of test statistics that are naturally modified to null statistics. We then obtain the uniformly most powerful (UMP) unbiased tests among these classes. If the underlying distribution is symmetric, the UMP unbiased test statistic is similar to that proposed by Pan (Bioinformatics 19:1333-1340, 2003). Under another condition, the UMP unbiased test statistic has a different formula with one more degree of freedom and therefore is expected to give a more powerful test and a more accurate p-value estimation from a modified null statistic. In microarray data, because the number of replicates is often small, differences in the degree of freedom will produce large effects on the power of test and the variance of the p-value estimation. Some simulation studies and real data analyses are illustrated to investigate the performances of the methods.

关键词： microarray data Multiple test Null statistic Small sample Uniformly most powerful unbiased test

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Gene regulatory network from microarray data of colon cancer patients using TSK-type recurrent neural fuzzy network

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GENE 2012年第2期506卷 408-416页

作者： Vineetha, S. Bhat, C. Chandra Shekara Idicula, Sumam Mary Rajiv Gandhi Inst Technol Dept Comp Sci Kottayam Kerala India Natl Inst Interdisciplinary Sci & Technol Trivandrum Kerala India Cochin Univ Sci & Technol Cochin 682016 Kerala India

In this work we applied a TSK-type recurrent neural fuzzy approach to extract regulatory relationship among genes and reconstruct gene regulatory network from microarray data. The identified signature has captured the regulatory relationship among 27 differentially expressed genes from microarray dataset. We applied three different methods viz., feed forward neural fuzzy, modified genetic algorithm and recurrent neural fuzzy, on the same data set for the inference of GRNs and the results obtained are almost comparable. In all tested cases, TRNFN identified more biologically meaningful relations. We found that 87.8% of the total interactions extracted by TRNFN are correct in accordance with the biological knowledge. Our analysis resulted in 2 major outcomes. First, upregulated genes are regulated by more genes than downregulated genes. Second, tumor activators activate other tumor activators and suppress tumor suppressers strongly in the disease environment. These findings will help to elucidate the common molecular mechanism of colon cancer, and provide new insights into cancer diagnostics, prognostics and therapy. (C) 2012 Elsevier B.V. All rights reserved.

关键词： microarray data Gene regulatory network Recurrent neural fuzzy network Fuzzy logic

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