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Validation of MIMGO: a method to identify differentially expressed GO terms in a microarray dataset.

BMC research notes 2012年第1期5卷 680-680页

作者： Yamada, Yoichi Sawada, Hiroki Hirotani, Ken-Ichi Oshima, Masanobu Satou, Kenji Institute of Science and Engineering Kanazawa University Kanazawa Japan

We previously proposed an algorithm for the identification of GO terms that commonly annotate genes whose expression is upregulated or downregulated in some microarray data compared with in other microarray data. We call these "differentially expressed GO terms" and have named the algorithm "matrix-assisted identification method of differentially expressed GO terms" (MIMGO). MIMGO can also identify microarray data in which genes annotated with a differentially expressed GO term are upregulated or downregulated. However, MIMGO has not yet been validated on a real microarray dataset using all available GO terms. We combined Gene Set Enrichment Analysis (GSEA) with MIMGO to identify differentially expressed GO terms in a yeast cell cycle microarray dataset. GSEA followed by MIMGO (GSEA + MIMGO) correctly identified (p < 0.05) microarray data in which genes annotated to differentially expressed GO terms are upregulated. We found that GSEA + MIMGO was slightly less effective than, or comparable to, GSEA (Pearson), a method that uses Pearson's correlation as a metric, at detecting true differentially expressed GO terms. However, unlike other methods including GSEA (Pearson), GSEA + MIMGO can comprehensively identify the microarray data in which genes annotated with a differentially expressed GO term are upregulated or downregulated. MIMGO is a reliable method to identify differentially expressed GO terms comprehensively.

关键词： Gene Ontology microarray Data microarray dataset Strict Threshold Ideal Expression Pattern

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Classification of ALL, AML and MLL leukaemia types on microarray dataset using LSTM neural network approach

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2023年第3期38卷 1299-1306页

作者： Akalin, Fatma Yumusak, Nejat Sakarya Univ Fac & Informat Sci Dept Informat Syst Engn TR-54187 Sakarya Turkiye Sakarya Univ Fac & Informat Sci Dept Comp Engn TR-54187 Sakarya Turkiye

In this study, it is realized to differentiate ALL, AML and MLL malignancies, which are the types of leukaemia, using the microarray dataset. The flow chart related to the classification of malignancies is given in Figure A. Figure A. Flow chart related to the classification of ALL, AML and MLL malignanciesPurpose: Classification of ALL, AML and MLL malignancies using LSTM neural network architecture on microarray *** and Methods: In this study, the microarray dataset obtained from https://*** /biolab/supp/bi-cancer/projections/ is used. microarray dataset, which has dimensional superiority, creates a complex and costly situation. For this reason, it is planned to facilitate the analysis process with computer -aided systems. In the presented study;ALL, AML and MLL malignancies are analyzed with an artificial intelligence-supported system. Firstly, qualified disease-associated genes are selected from the microarray dataset using the whale optimization algorithm in this scope. Then, ALL, AML and MLL malignancies are classified using the LSTM neural network on selected potential genes. This present study which has a simple hierarchy and low computational complexity produced an efficient ***: This study classified ALL, AML and MLL malignancies using the microarray dataset. In this context, LSTM neural network architecture was used on the potential genes selected with the whale optimization algorithm and an accuracy rate of 89.883% was ***: Diagnostic and prognostic prediction of leukaemia disease is important in terms of determining the category of the disease and planning the treatment process. In the current study conducted for this purpose, ALL, AML and MLL leukaemia types were distinguished successfully.

关键词： microarray dataset Whale optimization algorithm LSTM neural network

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A scalable association rule learning and recommendation algorithm for large-scale microarray datasets

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JOURNAL OF BIG DATA 2022年第1期9卷 1-25页

作者： Li, Haosong Sheu, Phillip C-Y Univ Calif Irvine Dept Elect Engn & Comp Sci Irvine CA 92697 USA

Association rule learning algorithms have been applied to microarray datasets to find association rules among genes. With the development of microarray technology, larger datasets have been generated recently that challenge the current association rule learning algorithms. Specifically, the large number of items per transaction significantly increases the running time and memory consumption of such tasks. In this paper, we propose the Scalable Association Rule Learning (SARL) heuristic that efficiently learns gene-disease association rules and gene-gene association rules from large-scale microarray datasets. The rules are ranked based on their importance. Our experiments show the SARL algorithm outperforms the Apriori algorithm by one to three orders of magnitude.

关键词： Association rule learning microarray dataset Frequent itemset mining Scalability Graph partitioning Apriori algorithm

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Adaptive Genetic Algorithm with Exploration-Exploitation Tradeoff for Preprocessing microarray datasets

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CURRENT BIOINFORMATICS 2017年第5期12卷 441-451页

作者： Rajappan, Sivaraj Rangasamy, DeviPriya Velalar Coll Engn & Technol Dept Comp Sci & Engn Erode Tamil Nadu India Kongu Engn Coll Dept Informat Technol Erode Tamil Nadu India

Background: microarray gene expression datasets contain huge volume of gene data to be used for cancer analysis but often suffer from "curse of dimensionality" and "missing values". They prevent analysts from extracting right knowledge and often results in instable results. Objective: To address both these issues, the paper proposes a novel algorithm based on Genetic Algorithm (GA). Method: GA is commonly used for feature selection and treating missing values in microarray datasets. But, it often results in premature convergence due to insufficient exploration and exploitation. In the proposed Adaptive Genetic Algorithm (AGA), genetic parameters are dynamically determined based on the values in current generation in order to improve optimality of the solution. The population is divided into two sub-populations and crossover and mutation are performed in parallel on these sub-populations in order to speed up the execution and also to have modularity in the population for performing these operations. In this paper, the missing values are first imputed using AGA and again AGA is used to select significant features. Results: The proposed methodology is implemented in different real microarray datasets to impute values at different missing proportions and to select prominent features. It is found that the datasets processed with AGA provides better results than the standard methods. Conclusion: AGA can be implemented successfully in all datasets where the number of features is large and missing values are present. AGA preprocesses the datasets and prepares them for better classification.

关键词： microarray dataset feature selection missing values genetic algorithm classification Adaptive Genetic Algorithm

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Feature selection techniques for microarray datasets:a comprehensive review,taxonomy,and future directions

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Frontiers of Information Technology & Electronic Engineering 2022年第10期23卷 1451-1478页

作者： Kulanthaivel BALAKRISHNAN Ramasamy DHANALAKSHMI Department of Computer Science and Engineering Indian Institute of Information TechnologyTiruchirappalli 620012India

For optimal results,retrieving a relevant feature from a microarray dataset has become a hot topic for researchers involved in the study of feature selection(FS)*** aim of this review is to provide a thorough description of various,recent FS *** review also focuses on the techniques proposed for microarray datasets to work on multiclass classification problems and on different ways to enhance the performance of learning *** attempt to understand and resolve the imbalance problem of datasets to substantiate the work of researchers working on microarray *** analysis of the literature paves the way for comprehending and highlighting the multitude of challenges and issues in finding the optimal feature subset using various FS techniques.A case study is provided to demonstrate the process of implementation,in which three microarray cancer datasets are used to evaluate the classification accuracy and convergence ability of several wrappers and hybrid algorithms to identify the optimal feature subset.

关键词： Feature selection High dimensionality Learning techniques microarray dataset

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Supervised feature selection on gene expression microarray datasets using manifold learning

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2023年第1期237卷

作者： Zare, Masoumeh Azizizadeh, Najmeh Kazemipour, Ali Shahid Bahonar Univ Kerman Res Inst Plant Prod Technol Kerman Iran Shahid Bahonar Univ Kerman Fac Math & Comp Dept Appl Math Kerman Iran Shahid Bahonar Univ Kerman Dept Agron & Plant Breeding Kerman Iran

In recent decades, the ultimate output from microarray assay, has produced enormous numbers of microarray datasets, regardless of the used technology. These datasets include complex and high dimensional samples and genes that the number of samples is much smaller than the number of genes (features). Due to the redundant dimensions in these datasets, processing them directly not only leads to poor performance but also increases computation time and memory usage. Feature selection reduces computational expense while improving or maintaining diagnosis accuracy. In this study, we propose a new supervised feature selection method based on a manifold learning approach. We focus in two different directions to address this issue. First, maximum relevancy criterion that achieves by integrating Supervised Laplacian Eigenmaps (S-LE) and a matrix, which can realize the process of feature selection. The applied criterion simultaneously opts the features that make same-class samples closer to each other and ignores the features that cause different-class samples be near. Second, minimum redundancy among selected features by applying the Pearson correlation coefficient. In the test phase, the proposed method is compared with ten state-of-the-art algorithms on seven microarray datasets. Reported results show that the proposed method has more promising performance than the other methods.

关键词： Supervised feature selection microarray dataset Discriminative features Redundant features

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Finding consistent disease subnetworks across microarray datasets

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BMC BIOINFORMATICS 2011年第13-sup期12卷 S15-S15页

作者： Soh, Donny Dong, Difeng Guo, Yike Wong, Limsoon Natl Univ Singapore Singapore 117417 Singapore Univ London Imperial Coll Sci Technol & Med London SW7 2BZ England Inst Infocomm Res Singapore 138632 Singapore

Background: While contemporary methods of microarray analysis are excellent tools for studying individual microarray datasets, they have a tendency to produce different results from different datasets of the same disease. We aim to solve this reproducibility problem by introducing a technique (SNet). SNet provides both quantitative and descriptive analysis of microarray datasets by identifying specific connected portions of pathways that are significant. We term such portions within pathways as "subnetworks". Results: We tested SNet on independent datasets of several diseases, including childhood ALL, DMD and lung cancer. For each of these diseases, we obtained two independent microarray datasets produced by distinct labs on distinct platforms. In each case, our technique consistently produced almost the same list of significant nontrivial subnetworks from two independent sets of microarray data. The gene-level agreement of these significant subnetworks was between 51.18% to 93.01%. In contrast, when the same pairs of microarray datasets were analysed using GSEA, t-test and SAM, this percentage fell between 2.38% to 28.90% for GSEA, 49.60% tp 73.01% for t-test, and 49.96% to 81.25% for SAM. Furthermore, the genes selected using these existing methods did not form subnetworks of substantial size. Thus it is more probable that the subnetworks selected by our technique can provide the researcher with more descriptive information on the portions of the pathway actually affected by the disease. Conclusions: These results clearly demonstrate that our technique generates significant subnetworks and genes that are more consistent and reproducible across datasets compared to the other popular methods available (GSEA, t-test and SAM). The large size of subnetworks which we generate indicates that they are generally more biologically significant (less likely to be spurious). In addition, we have chosen two sample subnetworks and validated them with references from biological l

关键词： Duchenne Muscular Dystrophy Gene List Acute Lymphoblastic Leukaemia microarray dataset Duchenne Muscular Dystrophy

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CARMA: A platform for analyzing microarray datasets that incorporate replicate measures

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BMC BIOINFORMATICS 2006年第1期7卷 149-149页

作者： Greer, KA McReynolds, MR Brooks, HL Hoying, JB Univ Arizona Genom Res Lab Biomed Engn Program Tucson AZ 85724 USA Univ Arizona Coll Med Dept Physiol Tucson AZ 85724 USA

Background: The incorporation of statistical models that account for experimental variability provides a necessary framework for the interpretation of microarray data. A robust experimental design coupled with an analysis of variance ( ANOVA) incorporating a model that accounts for known sources of experimental variability can significantly improve the determination of differences in gene expression and estimations of their significance. Results: To realize the full benefits of performing analysis of variance on microarray data we have developed CARMA, a microarray analysis platform that reads data files generated by most microarray image processing software packages, performs ANOVA using a user- defined linear model, and produces easily interpretable graphical and numeric results. No pre- processing of the data is required and user- specified parameters control most aspects of the analysis including statistical significance criterion. The software also performs location and intensity dependent lowess normalization, automatic outlier detection and removal, and accommodates missing data. Conclusion: CARMA provides a clear quantitative and statistical characterization of each measured gene that can be used to assess marginally acceptable measures and improve confidence in the interpretation of microarray results. Overall, applying CARMA to microarray datasets incorporating repeated measures effectively reduces the number of gene incorrectly identified as differentially expressed and results in a more robust and reliable analysis.

关键词： Background Subtraction Outlier Detection microarray dataset Graphical Output Lowess Normalization

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Dual regularized subspace learning using adaptive graph learning and rank constraint: Unsupervised feature selection on gene expression microarray datasets

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COMPUTERS IN BIOLOGY AND MEDICINE 2023年 167卷 107659-107659页

作者： Moslemi, Amir Ahmadian, Arash Sunnybrook Hlth Sci Ctr Imaging Res & Phys Sci Toronto ON Canada Univ Toronto Edward S Rogers Sr Dept Elect & Comp Engn Toronto ON Canada

High-dimensional problems have increasingly drawn attention in gene selection and analysis. To add insult to injury, usually the number of features is greater than number of samples in microarray gene dataset which leads to an ill-posed underdetermined equation system. Poor performance and high computational time for learning algorithms are consequences of redundant features in high-dimensional data. Feature selection is a noteworthy pre-processing method to ameliorate the curse of dimensionality with aim of maximum relevancy and minimum redundancy information preservation. Likewise, unsupervised feature selection has been important since collecting labels for data is expensive. In this paper, we develop a novel robust unsupervised feature selection to select discriminative subset of features for unlabeled data based on rank constrained and dual regularized nonnegative matrix factorization. The major focus of the proposed technique is to discard redundant features while keeping the informative features. Proposed feature selection technique consists of nonnegative matrix factorization to decompose the data into feature weight matrix and representation matrix, inner product norm as regularization for both feature weight matrix and representation matrix, adaptive structure learning to preserve local information and Schatten-p norm as rank constraint. To demonstrate the effectiveness of the proposed method, numerical studies are conducted on six benchmark microarray datasets. The results show that the proposed technique outperforms eight state-of-art unsupervised feature selection techniques in terms of clustering accuracy and normalized mutual information.

关键词： Gene selection microarray dataset Unsupervised feature selection Nonnegative matrix factorization Inner product norm Local information preserving Rank constraint

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A meta-analysis of kidney microarray datasets: investigation of cytokine gene detection and correlation with rt-PCR and detection thresholds

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BMC GENOMICS 2007年第1期8卷 88-88页

作者： Park, Walter D. Stegall, Mark D. Mayo Clin Coll Med Dept Surg Rochester MN USA

Background: microarrays provide a means to simultaneously examine the gene expression of the entire transcriptome in a single sample. Many studies have highlighted the need for novel software and statistical approaches to assess the measured gene expression. Less attention has been directed toward whether genes considered undetectable by microarray can be detected by other strategies or whether these genes can provide accurate gene expression determinations. In the kidney this is a concern for genes such as cytokines which dramatically influence the immune response but are often considered low abundance genes produced by a small number of cells. Results: Using both publicly available and our own microarray datasets we analyzed the detection p-value and detection call values for 81 human kidney samples run on the U133A or U133Plus2.0 Affymetrix microarrays (Affymetrix, Santa Clara, CA). For the cytokine genes, the frequency of detection in each sample group (normal, transplant and renal cell carcinoma) was examined and revealed that a majority of cytokine related genes are not detectable in human kidney by microarray. Using a subset of 29 Mayo transplant samples, a group of seven transplant-related cytokines and eight non-cytokine genes were evaluated by real-time PCR (rt-PCR). For these 15 genes we compared the impact of decreasing microarray detection frequency with the changes in gene expression observed by both microarray and rt-PCR. We found that as microarray detection frequency decreased the correlation between microarray and rt-PCR data also decreased. Conclusion: We conclude that, when analyzing microarray data from human kidney samples, genes generally expressed at low abundance (i.e. cytokines) should be evaluated with more sensitive approaches such as rt-PCR. In addition, our data suggest that the use of detection frequency cutoffs for inclusion or exclusion of microarray data may be appropriate when comparing microarray and rt-PCR gene expression data an

关键词： Renal Cell Carcinoma Cytokine Gene Human Kidney microarray dataset Detection Frequency

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