Showing 401 - 420 results of 702 for search '(selection OR detection) microarray', query time: 0.09s Refine Results
  1. 401

    Exosomal circRNA-0008302 from Adipose-derived Stem Cells Protects Against Myocardial Injury by Chengyan Hu, Shuai Wang, Yanling Wang, Zhenxing Fan, Xipeng Sun, Zhi Liu

    Published 2024-02-01
    “…The expression levels of miR-466i-5p were evaluated, and western blotting was performed to detect the expression of methionine sulfoxide reductase A (MsrA) protein. …”
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    Article
  2. 402

    Parallel clustering algorithm for large-scale biological data sets. by Minchao Wang, Wu Zhang, Wang Ding, Dongbo Dai, Huiran Zhang, Hao Xie, Luonan Chen, Yike Guo, Jiang Xie

    Published 2014-01-01
    “…The parallel affinity propagation also achieves a good performance when clustering large-scale gene data (microarray) and detecting families in large protein superfamilies.…”
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    Article
  3. 403

    General Approach to Identifying Potential Targets for Cancer Imaging by Integrated Bioinformatics Analysis of Publicly Available Genomic Profiles by Yongliang Yang, S. James Adelstein, Amin I. Kassis

    Published 2011-03-01
    “…Here we present a simple and integrated bioinformatics analysis approach that assembles a public cancer microarray database with a pathway knowledge base for ascertaining and prioritizing upregulated genes encoding cell surface- or membrane-bound proteins, which could serve imaging targets. …”
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    Article
  4. 404

    Transcription factor binding site analysis identifies FOXO transcription factors as regulators of the cutaneous wound healing process. by Karl Markus Roupé, Srinivas Veerla, Joshua Olson, Erica L Stone, Ole E Sørensen, Stephen M Hedrick, Victor Nizet

    Published 2014-01-01
    “…The search for significantly overrepresented and co-occurring transcription factor binding sites in the promoter regions of the most differentially expressed genes in microarray data sets could be a powerful approach for finding key regulators of complex biological processes. …”
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    Article
  5. 405

    CHD5, a brain-specific paralog of Mi2 chromatin remodeling enzymes, regulates expression of neuronal genes. by Rebecca Casaday Potts, Peisu Zhang, Andrea L Wurster, Patricia Precht, Mohamed R Mughal, William H Wood, Yonqing Zhang, Kevin G Becker, Mark P Mattson, Michael J Pazin

    Published 2011-01-01
    “…CHD5 protein was predominantly nuclear in primary rat neurons and brain sections. Microarray analysis revealed genes that were upregulated and downregulated when CHD5 was depleted from primary neurons. …”
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    Article
  6. 406

    Maternal bisphenol a exposure impacts the fetal heart transcriptome. by Kalyan C Chapalamadugu, Catherine A Vandevoort, Matthew L Settles, Barrie D Robison, Gordon K Murdoch

    Published 2014-01-01
    “…At the end of treatment, fetal heart tissues were collected and chamber specific transcriptome expression was assessed using genome-wide microarray. Quantitative real-time PCR was conducted on select genes and ventricular tissue glycogen content was quantified. …”
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    Article
  7. 407

    Identification and validation of TSPAN13 as a novel temozolomide resistance-related gene prognostic biomarker in glioblastoma. by Haofei Wang, Zhen Liu, Zesheng Peng, Peng Lv, Peng Fu, Xiaobing Jiang

    Published 2025-01-01
    “…Using LASSO Cox analysis, we selected 12 TMZR-RDEGs to construct a risk score model, which was evaluated for performance through survival analysis, time-dependent ROC, and stratified analyses. …”
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    Article
  8. 408

    Weighted frequent gene co-expression network mining to identify genes involved in genome stability. by Jie Zhang, Kewei Lu, Yang Xiang, Muhtadi Islam, Shweta Kotian, Zeina Kais, Cindy Lee, Mansi Arora, Hui-Wen Liu, Jeffrey D Parvin, Kun Huang

    Published 2012-01-01
    “…In this study, we used a network mining algorithm to identify tightly connected gene co-expression networks that are frequently present in microarray datasets from 33 types of cancer which were derived from 16 organs/tissues. …”
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    Article
  9. 409

    Have maternal or paternal ages any impact on the prenatal incidence of genomic copy number variants associated with fetal structural anomalies? by Marta Larroya, Marta Tortajada, Eduard Mensión, Montse Pauta, Laia Rodriguez-Revenga, Antoni Borrell

    Published 2021-01-01
    “…We conducted a non-paired case-control study (1:2 ratio) among pregnancies undergoing chromosomal microarray analysis (CMA) because of fetal ultrasound anomalies, from December 2012 to May 2020. …”
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    Article
  10. 410

    Identification of the Exercise and Time Effects on Human Skeletal Muscle through Bioinformatics Methods by Mufang Feng, Jie Ji, Xiaoliu Li, Xinming Ye

    Published 2022-01-01
    “…To determine the effects of exercise and time on human skeletal muscle, we downloaded the microarray expression profile of GSE1832 and analyzed it to select differentially expressed genes (DEGs). …”
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    Article
  11. 411

    Cuproptosis genes in predicting the occurrence of allergic rhinitis and pharmacological treatment. by Ting Yi

    Published 2025-01-01
    “…<h4>Results</h4>Four AR signature genes (MRPS30, CLPX, MRPL13, and MRPL53) were selected by the MCC, EPC, BottleNeck, and Closeness algorithms. …”
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    Article
  12. 412

    Bioinformatic-based differential expression gene expressions of epithelial mesenchymal transformation in diabetic kidney disease and prediction of traditional Chinese medicine by Liu Wu, Zhou Yi, Yu Fang-ning, Zhang Ning

    Published 2022-11-01
    “…ObjectiveBased upon the bioinformatic analysis of gene chip data between patients with diabetic kidney disease (DKD) and normal controls, differentially expressed genes of epithelial mesenchymal transformation of DKD were screened for elucidating its pathogenesis and predicting the potential therapeutic Chinese medicine for DKD.MethodsGSE23338 microarray data were downloaded from gene expression omnibus, related differentially expressed genes screened by R language and differentially expressed genes analyzed by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes. …”
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    Article
  13. 413

    Cellular senescence-associated genes in rheumatoid arthritis: Identification and functional analysis. by You Ao, Qing Lan, Tianhua Yu, Zhichao Wang, Jing Zhang

    Published 2025-01-01
    “…In our study, we analyzed RA microarray data from the Gene Expression Omnibus (GEO) and focused on cellular senescence genes from the CellAge database. …”
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    Article
  14. 414
  15. 415

    DNA familial binding profiles made easy: comparison of various motif alignment and clustering strategies. by Shaun Mahony, Philip E Auron, Panayiotis V Benos

    Published 2007-03-01
    “…Prediction of the identity or the structural class of a protein that binds to a given DNA pattern will enhance the analysis of microarray and ChIP-chip data where frequently multiple putative targets of usually unknown TFs are predicted. …”
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  16. 416

    MicroRNA biomarkers of type 2 diabetes: A protocol for corroborating evidence by computational genomics and meta-analyses. by Hongmei Zhu, Siu-Wai Leung

    Published 2021-01-01
    “…Biologically relevant microRNAs will then be selected through genomic database corroboration. Their association with T2D is further measured by area under the curve (AUC) of receive operating characteristic (ROC). …”
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    Article
  17. 417

    Integrated Bioinformatics Analysis of Hub Genes and Pathways in Anaplastic Thyroid Carcinomas by Xueren Gao, Jianguo Wang, Shulong Zhang

    Published 2019-01-01
    “…The aim of the present study was to identify hub genes and pathways in ATC by microarray expression profiling. Two independent datasets (GSE27155 and GSE53072) were downloaded from GEO database. …”
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    Article
  18. 418

    Bioinformatic identification of Single Nucleotide Polymorphisms (SNPs) in keratin-associated protein genes in alpacas (Vicugna pacos) by Deyanira Figueroa, Manuel More, Gustavo Gutiérrez, F. Abel Ponce de León

    Published 2024-03-01
    “…Of these, 35 SNPs were included in the 76K alpaca SNP microarray and 32 SNPs were confirmed in a population of 936 alpacas.…”
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  19. 419

    Reverse engineering a hierarchical regulatory network downstream of oncogenic KRAS by Iwona Stelniec‐Klotz, Stefan Legewie, Oleg Tchernitsa, Franziska Witzel, Bertram Klinger, Christine Sers, Hanspeter Herzel, Nils Blüthgen, Reinhold Schäfer

    Published 2012-07-01
    “…We measured mRNA and protein levels in manipulated cells by microarray, RT–PCR and western blot analysis, respectively. …”
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  20. 420

    Gene Profiling of Aortic Valve Interstitial Cells under Elevated Pressure Conditions: Modulation of Inflammatory Gene Networks by James N. Warnock, Bindu Nanduri, Carol A. Pregonero Gamez, Juliet Tang, Daniel Koback, William M. Muir, Shane C. Burgess

    Published 2011-01-01
    “…Linear, two-cycle amplification of total RNA, followed by microarray was performed for transcriptome analysis (with qRT-PCR validation). …”
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    Article