Showing 41 - 60 results of 702 for search '(( detection microarray ) OR ((( selective microarray ) OR ( selection microarray ))))*', query time: 0.14s Refine Results
  1. 41

    A tailored lectin microarray for rapid glycan profiling of therapeutic monoclonal antibodies by Shen Luo, Baolin Zhang

    Published 2024-12-01
    “…In this study, we introduce a custom-designed lectin microarray featuring nine distinct lectins: rPhoSL, rOTH3, RCA120, rMan2, MAL_I, rPSL1a, PHAE, rMOA, and PHAL. …”
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  2. 42

    CRISPR screens and lectin microarrays identify high mannose N-glycan regulators by C. Kimberly Tsui, Nicholas Twells, Jenni Durieux, Emma Doan, Jacqueline Woo, Noosha Khosrojerdi, Janiya Brooks, Ayodeji Kulepa, Brant Webster, Lara K. Mahal, Andrew Dillin

    Published 2024-11-01
    “…We used CRISPR screens to uncover the expanded network of genes controlling high mannose levels, followed by lectin microarrays to fully measure the complex effect of select regulators on glycosylation globally. …”
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  3. 43

    THE VALIDATION OF THE RESULTS OF MICROARRAY STUDIES OF ASSOCIATION BETWEEN GENE POLYMORPHISMS AND THE FREQUENCY OF RADIATION EXPOSURE MARKERS by M. V. Khalyuzova, N. V. Litvyakov, A. E. Sazonov, Ye. N. Albakh, D. S. Isubakova, A. B. Karpov, R. M. Takhauov

    Published 2014-06-01
    “…The results from the selective validation research into the association between genetic polymorphisms and the frequency of cytogenetic abnormalities on a large independent sample are analyzed. …”
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    Discovery of possible gene relationships through the application of self-organizing maps to DNA microarray databases. by Rocio Chavez-Alvarez, Arturo Chavoya, Andres Mendez-Vazquez

    Published 2014-01-01
    “…DNA microarrays and cell cycle synchronization experiments have made possible the study of the mechanisms of cell cycle regulation of Saccharomyces cerevisiae by simultaneously monitoring the expression levels of thousands of genes at specific time points. …”
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    Article
  6. 46

    Molecular sub-classification of renal epithelial tumors using meta-analysis of gene expression microarrays. by Thomas Sanford, Paul H Chung, Ariel Reinish, Vladimir Valera, Ramaprasad Srinivasan, W Marston Linehan, Gennady Bratslavsky

    Published 2011-01-01
    “…<h4>Experimental design</h4>A search of publicly available databases was performed to identify microarray datasets with multiple histologic sub-types of renal cortical neoplasms. …”
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  7. 47

    Microarray Core Detection by Geometric Restoration by Jimmy C. Azar, Christer Busch, Ingrid B. Carlbom

    Published 2012-01-01
    “…Furthermore, the algorithm is more efficient than existing methods based on the Hough transform for circle detection. The algorithm’s simplicity, accuracy, and computational efficiency allow for automated high-throughput analysis of microarray images.…”
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  8. 48

    Expression of long non-coding RNA in patients with non-IgA mesangial proliferative glomerulonephritis by CONG Shan, SUI Wei-guo, ZOU Gui-mian, XUE Wen, LI Huan, YAN Qiang, CHEN Jie-jing, LUO Ya-dan, CHEN Huai-zhou

    Published 2015-01-01
    “…Objective To study differential expression profile of mRNA and long non-coding RNA(IncRNA) through microarray analysis between non-IgA mesangial proliferative glomerulonephritis(MsPGN) patients and the controls,and then explore the potential role of IncRNA in the pathogenesis of non-IgA MsPGN.Methods Through simple random sampling,4 patients with non-IgA MsPGN and 2 controls were selected as disease group and control group,respectively.Renal cortical tissues from two groups were collected.Total RNA was extracted,quantified and prepared to ds-cDNA through reverse transcription ds-cDNA was labeled with NimbleGen one-color DNA labeling kit and used for array hybridization.All experimental data were processed through GO analysis,Pathway analysis and the gene loci correlation analysis of mRNA and IncRNA.Some IncRNAs that were closely related to non-IgA MsPGN were screened out.Finally,part of the array results was detected by PCR to verify the reliability of array test Results By fold change filtering,4317 differentially expressed mRNAs and 3502 differentially expressed IncRNAs were screened out.Five IncRNAs were found to play potential roles in the pathogenesis of non-IgA MsPGN:AF1180924(close to coding gene FGG),AK092233(close to coding gene COL18A1),AK130579(close to coding gene CREBBP),AK023598(close to coding gene LEPR),and AK055915(close to coding gene CDC42EP3).These results provided an important basis for revealing the pathogenesis of non-IgA MsPGN.Conclusions Some IncRNAs can potentially regulate related genes and plays an important role in the pathogenesis and development of non-IgA MsPGN.…”
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  9. 49

    Carbohydrate Microarrays Identify Blood Group Precursor Cryptic Epitopes as Potential Immunological Targets of Breast Cancer by Denong Wang, Jin Tang, Shaoyi Liu, Jiaoti Huang

    Published 2015-01-01
    “…Using carbohydrate microarrays, we explored potential natural ligands of antitumor monoclonal antibody HAE3. …”
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    IDENTIFYING IMPORTANT GENES IN OVARIAN CANCER FROM HIGH-DIMENSIONAL MICROARRAY DATA USING SIFS-CART METHOD by Ni Kadek Emik Sapitri, Umu Sa'adah, Nur Shofianah

    Published 2024-07-01
    “…Ovarian cancer can be identified from microarray data using machine learning. Many studies only focus on improving the machine learning classification algorithms to achieve higher performance. …”
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  13. 53

    Photopolymerization as an innovative detection technique for low-density microarrays by Laura R. Kuck, Amber W. Taylor

    Published 2008-08-01
    “…One limitation that accounts in part for the scarcity of commercially available diagnostic microarrays is the expense associated with fluorescence detection. …”
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  14. 54

    RETRACTED ARTICLE: Multi-stage biomedical feature selection extraction algorithm for cancer detection by Ismail Keshta, Pallavi Sagar Deshpande, Mohammad Shabaz, Mukesh Soni, Mohit kumar Bhadla, Yasser Muhammed

    Published 2023-04-01
    “…Abstract Cancer is a significant cause of death worldwide. Early cancer detection is greatly aided by machine learning and artificial intelligence (AI) to gene microarray data sets (microarray data). …”
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  15. 55

    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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    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
    “…Here we show, and experimentally verify, that cryptic repetitive element probes present in Illumina and Affymetrix gene expression microarray platforms can accurately and sensitively monitor repetitive element expression data. …”
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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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  20. 60

    CCD Standard Curve Fitting for Microarray Detection Base on Multi-Layer Perceptron by Zhenhua Gan, Dongyu He, Peishu Wu, Baoping Xiong, Nianyin Zeng, Fumin Zou, Feng Guo, Qin Bao, Fengyan Zhao

    Published 2024-01-01
    “…The gray-level of the fluorescent probe in detection image was obtained as the data set acquired by the microarray scanner at different exposure time. …”
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