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Showing 81 - 100 results of 321 for search '(( selection microarray ) OR ( selective microarray ))*', query time: 0.11s Refine Results
  1. 81

    Themis2/ICB1 is a signaling scaffold that selectively regulates macrophage Toll-like receptor signaling and cytokine production. by Matthew J Peirce, Matthew Brook, Nicholas Morrice, Robert Snelgrove, Shajna Begum, Alessandra Lanfrancotti, Clare Notley, Tracy Hussell, Andrew P Cope, Robin Wait

    Published 2010-07-01
    “…<h4>Background</h4>Thymocyte expressed molecule involved in selection 1 (Themis1, SwissProt accession number Q8BGW0) is the recently characterised founder member of a novel family of proteins. …”
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  2. 82
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  5. 85

    Exposing Optimal Feature Sets for Enhancing Machine Learning Performance by Hiba Mohammed Al-Marwai, Ghaleb H. Al-Gaphari, Mohammed Mohammed Zayed

    Published 2025-01-01
    “…The majority of high dimensional gene expression data contain a significant amount of redundant genes, posing challenges for machine learning algorithms due to their high dimensionality. Feature selection has shown to be a successful method for improving classification algorithms performance by addressing two primary objectives: reducing the number of features and improving classification accuracy. …”
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  6. 86
  7. 87

    Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data. by Enrico Glaab, Jaume Bacardit, Jonathan M Garibaldi, Natalio Krasnogor

    Published 2012-01-01
    “…A comparison with other benchmark microarray sample classifiers based on three diverse feature selection algorithms suggests that these evolutionary learning techniques can compete with state-of-the-art methods like support vector machines. …”
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  8. 88

    Evaluation of gene expression classification studies: factors associated with classification performance. by Putri W Novianti, Kit C B Roes, Marinus J C Eijkemans

    Published 2014-01-01
    “…The MAQC II study on cancer classification problems has found that performance was affected by factors such as the classification algorithm, cross validation method, number of genes, and gene selection method. In this paper, we study the hypothesis that the disease under study significantly determines which method is optimal, and that additionally sample size, class imbalance, type of medical question (diagnostic, prognostic or treatment response), and microarray platform are potentially influential. …”
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  9. 89

    Bioinformatics meets machine learning: identifying circulating biomarkers for vitiligo across blood and tissues by Qiyu Wang, Jingwei Yuan, Jingwei Yuan, Mengdi Zhang, Haiyan Jia, Hongjie Lu, Yan Wu

    Published 2025-05-01
    “…The exact aetiology and pathogenesis of vitiligo remain incompletely understood.MethodsFirst, a microarray dataset of blood samples from multiple patients with vitiligo was collected from GEO database.The limma package was used to analyze the microarray data and identify significant differentially expressed genes (DEGs). …”
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  10. 90

    A multi-omic meta-analysis reveals novel mechanisms of insecticide resistance in malaria vectors by Sanjay C. Nagi, Victoria A. Ingham

    Published 2025-05-01
    “…This study, employing a cross-species approach, integrates RNA-Sequencing, whole-genome sequencing, and microarray data to elucidate drivers of insecticide resistance in Anopheles gambiae complex and An. funestus. …”
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  11. 91

    The impact of the nucleosome code on protein-coding sequence evolution in yeast. by Tobias Warnecke, Nizar N Batada, Laurence D Hurst

    Published 2008-11-01
    “…We identify nucleosome positioning as a likely candidate to set up such a DNA-level selective regime and use high-resolution microarray data in yeast to compare the evolution of coding sequence bound to or free from nucleosomes. …”
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  12. 92

    A RNA-Seq Analysis of the Rat Supraoptic Nucleus Transcriptome: Effects of Salt Loading on Gene Expression. by Kory R Johnson, C C T Hindmarch, Yasmmyn D Salinas, YiJun Shi, Michael Greenwood, See Ziau Hoe, David Murphy, Harold Gainer

    Published 2015-01-01
    “…In addition, we compare the SON transcriptomes resolved by RNA-Seq methods with the SON transcriptomes determined by Affymetrix microarray methods in rats under the same osmotic conditions, and find that there are 6,466 genes present in the SON that are represented in both data sets, although 1,040 of the expressed genes were found only in the microarray data, and 2,762 of the expressed genes are selectively found in the RNA-Seq data and not the microarray data. …”
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  13. 93

    Aberrant expression of shared master-key genes contributes to the immunopathogenesis in patients with juvenile spondyloarthritis. by Lovro Lamot, Fran Borovecki, Lana Tambic Bukovac, Mandica Vidovic, Marija Perica, Kristina Gotovac, Miroslav Harjacek

    Published 2014-01-01
    “…Microarray results and bioinformatical analysis revealed 745 differentially expressed genes involved in various inflammatory processes, while qRT-PCR analysis of selected genes confirmed data universality and specificity of expression profiles in jSpA patients. …”
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  14. 94

    Analysis of Gene Expression in Human Dermal Fibroblasts Treated with Senescence-Modulating COX Inhibitors by Jeong A. Han, Jong-Il Kim

    Published 2017-06-01
    “…In contrast, celecoxib, another COX-2–selective inhibitor, and aspirin, a non-selective COX inhibitor, accelerated the senescence and aging. …”
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  15. 95

    An Ensemble Classification Method for High-Dimensional Data Using Neighborhood Rough Set by Jing Zhang, Guang Lu, Jiaquan Li, Chuanwen Li

    Published 2021-01-01
    “…Efficient and effective sample classification and feature selection are challenging tasks due to high dimensionality and small sample size of microarray data. …”
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  16. 96

    Two stable variants of Burkholderia pseudomallei strain MSHR5848 express broadly divergent in vitro phenotypes associated with their virulence differences. by A A Shea, R C Bernhards, C K Cote, C J Chase, J W Koehler, C P Klimko, J T Ladner, D A Rozak, M J Wolcott, D P Fetterer, S J Kern, G I Koroleva, S P Lovett, G F Palacios, R G Toothman, J A Bozue, P L Worsham, S L Welkos

    Published 2017-01-01
    “…Microscopic and colony morphology differences on six differential media were observed and only the Rough variant metabolized sugars in selective agar. Antimicrobial susceptibilities and lipopolysaccharide (LPS) features were characterized and phenotype microarray profiles revealed distinct metabolic and susceptibility disparities between the variants. …”
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  17. 97

    Nanomaterials based biosensors applied for detection of aflatoxin B1 in cereals: a review by Loyce Namanya, Emma Panzi Mukhokosi, Ediriisa Mugampoza

    Published 2025-01-01
    “…However, more studies are needed to address the automatic simultaneous detection of various aflatoxins in real samples and a biosensing system that integrates with microarray technology.…”
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  18. 98

    Genome-wide analysis of m6A-modified circRNAs in the mouse model of myocardial injury induced by obstructive sleep apnea by Jiuhuang Lan, Yuhui Wang, Chang Liu, Hongli Chen, Qingshi Chen

    Published 2025-04-01
    “…Methods Firstly, the expression profiles of m6A-modified circRNA in left ventricular tissue of the CIH mouse model were examined using circRNA microarray analysis. Then, the expression level of selected circrRNA was compared by folding change filtration, and the consistency between them and microarray results was verified by MeRIP-qPCR. …”
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  19. 99

    On-Chip DNA Assembly via Dielectrophoresis by Xichuan Rui, Lin-Sheng Wu, Xin Zhao

    Published 2025-01-01
    “…This work introduces a novel on-chip DNA assembly method via dielectrophoresis (DEP) that can potentially be integrated with microarray-based oligo synthesis on the same chip. Our DEP chip can selectively manipulate oligos and guide their movement without perturbing the surrounding fluid medium, thus aiding in DNA assembly. …”
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  20. 100

    SLEPR: a sample-level enrichment-based pathway ranking method -- seeking biological themes through pathway-level consistency. by Ming Yi, Robert M Stephens

    Published 2008-09-01
    “…Analysis of microarray and other high throughput data often involves identification of genes consistently up or down-regulated across samples as the first step in extraction of biological meaning. …”
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