Showing 61 - 80 results of 322 for search '(( elective microarray ) OR ( (selection OR selective) microarray ))', query time: 0.12s Refine Results
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    Silver coated porous silicon microarray SERS platform for detecting aflatoxin B1 fumonisin B1 and ochratoxin A by Rohit Kumar Singh, Narsingh R. Nirala, Sudharsan Sadhasivam, Divagar Muthukumar, Edward Sionov, Giorgi Shtenberg

    Published 2025-08-01
    “…The optical output was inversely proportional to the analyte concentration upon selective biorecognition by the anti-target mycotoxin aptamer-modified scaffold. …”
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    Article
  3. 63

    Multiplex peptide microarray profiling of antibody reactivity against neglected tropical diseases derived B-cell epitopes for serodiagnosis in Zimbabwe. by Arthur Vengesai, Thajasvarie Naicker, Herald Midzi, Maritha Kasambala, Tariro L Mduluza-Jokonya, Simbarashe Rusakaniko, Francisca Mutapi, Takafira Mduluza

    Published 2022-01-01
    “…In this framework, we present a pilot study to design and produce a peptide microarray for the integrated surveillance of neglected tropical diseases. …”
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    Prenatal diagnosis and molecular cytogenetic analysis of pure chromosome 10p15.3 microdeletion using chromosomal microarray analysis by Na Zhang, Nan Huang, Yu’e Chen, Xinying Chen, Jianlong Zhuang

    Published 2024-12-01
    “…Karyotyping and chromosomal microarray analysis (CMA) was conducted to assess chromosomal abnormalities and detect copy number variations (CNVs) within the families, respectively. …”
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    Microarray analysis of LTR retrotransposon silencing identifies Hdac1 as a regulator of retrotransposon expression in mouse embryonic stem cells. by Judith Reichmann, James H Crichton, Monika J Madej, Mary Taggart, Philippe Gautier, Jose Luis Garcia-Perez, Richard R Meehan, Ian R Adams

    Published 2012-01-01
    “…However, although silencing of selected individual retrotransposons can be relatively well-studied, many mammalian retrotransposons are seldom analysed and their silencing in germ cells, pluripotent cells or somatic cells remains poorly understood. …”
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  7. 67

    Effective detection of 148 cases chromosomal mosaicism by karyotyping, chromosomal microarray analysis and QF-PCR in 32,967 prenatal diagnoses by Yi Deng, Lan Zeng, Zhiling Wu, Jin Wang, Mengling Ye, Chun Chen, Ping Wei, Danni Wang, Guangming Deng, Shuyao Zhu

    Published 2025-04-01
    “…Methods A total of 148 fetuses diagnosed with chromosomal mosaicism by karyotyping with copy number variant sequencing (CNV-seq)/ chromosomal microarray analysis (CMA) and quantitative fluorescent polymerase chain reaction (QF-PCR) were selected, and the results from three the methods were compared and further analyzed. …”
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    Candidate Anxiety‐Related Genes in the Hippocampus of Hatano Male Rats: Anxiolytic Action of Neuromedin U in the Hippocampus by Kaito Sato, Atsuhiro Ishii, Shohei Kobayashi, Taichi Hatakeyama, Gen Watanabe, Tomoko Soga, Ishwar Parhar, Takashi Matsuwaki, Shogo Moriya, Ryo Ohta, Shuichi Chiba, Maiko Kawaguchi

    Published 2025-06-01
    “…The present study focuses on the hippocampus, which is associated with anxiety‐like behavior, and used microarray analysis and RT‐qPCR to select genes with differential expression in the hippocampus between HAA and LAA (Experiment 1). …”
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    GPC3 as a potential diagnostic and prognostic marker for lung adenocarcinoma by Wei-qin Wu, Qing-song Sun, Li-li Gao, Ya-juan Jia, Hong-mei Zhao, Hong Sun, Xiang Han

    Published 2024-12-01
    “…Four gene expression profiles were downloaded from GEO and merged into a training cohort, and those genes that were differentially expressed between LUAD and normal samples were selected. We performed LASSO regression, SVM-RFE, and ROC curve analyses, and external validations were conducted using the GSE115002 dataset, TCGA + GTEx datasets, and tissue microarrays (TMAs) of 56 patients with LUAD from our hospital. …”
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    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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