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Showing 21 - 40 results of 814 for search '(( effective microarray ) OR ( (selection OR selective) microarray ))', query time: 0.18s 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
    “…Herein, we present a newly developed nanostructured microarray based on silver-coated porous silicon (Ag-pSi) used as a surface-enhanced Raman scattering (SERS) transducer. …”
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    Article
  4. 24

    A two stage grading approach for feature selection and classification of microarray data using Pareto based feature ranking techniques: A case study by Rasmita Dash

    Published 2020-02-01
    “…High dimensional search space in microarray data with large number of genes and few dozen of samples increases the complexity of analysis of such databases. …”
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    Article
  5. 25

    A Novel Ensemble Feature Selection Technique for Cancer Classification Using Logarithmic Rank Aggregation Method by Hüseyin Öztoprak, Hüseyin Güney

    Published 2024-04-01
    “…Recent studies have shown that ensemble feature selection (EFS) has achieved outstanding performance in microarray data classification. …”
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    Article
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    Genetic Comparison and Selection of Reproductive and Growth-Related Traits in Qinchuan Cattle and Two Belgian Cattle Breeds by Xiaopeng Li, Peng Niu, Xueyan Wang, Fei Huang, Jieru Wang, Huimin Qu, Chunmei Han, Qinghua Gao

    Published 2025-02-01
    “…A total of 270 Belgian cattle (91 BR and 179 BWR) and 286 Qinchuan cattle were genotyped using the Illumina Bovine SNP 50K microarray. Data analysis was conducted using PLINK and Beagle 5.1 to estimate linkage disequilibrium (LD) and effective population size (Ne). …”
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    Article
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    Identification and validation of a novel autoantibody biomarker panel for differential diagnosis of pancreatic ductal adenocarcinoma by Metoboroghene O. Mowoe, Metoboroghene O. Mowoe, Hisham Allam, Joshua Nqada, Marc Bernon, Karan Gandhi, Sean Burmeister, Urda Kotze, Miriam Kahn, Christo Kloppers, Suba Dharshanan, Zafirah Azween, Pamela Maimela, Paul Townsend, Eduard Jonas, Jonathan M. Blackburn, Jonathan M. Blackburn

    Published 2025-01-01
    “…Specifically, we quantified the serological AAb profiles of 94 PDAC, chronic pancreatitis (CP), other pancreatic- (PC) and prostate cancers (PRC), non-ulcer dyspepsia patients (DYS), and healthy controls (HC).ResultsCombinatorial ROC curve analysis on the training cohort data from the cancer antigen microarrays identified the most effective biomarker combination as CEACAM1-DPPA2-DPPA3-MAGEA4-SRC-TPBG-XAGE3 with an AUC = 85·0% (SE = 0·828, SP = 0·684). …”
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  9. 29

    The Comparison of Three Measures in Feature Selection by SONG Zhi-chao, KANG Jian, SUN Guang-lu, HE Yong-jun

    Published 2018-02-01
    “…Three representative linear or nonlinear measures,linear correlation coefficient,symmetrical uncertainty,and mutual information are selected. By combining them with the fast correlation-based filter ( FCBF) feature selection method,we make the comparison of selected feature subset from 8 gene microarray and image datasets. …”
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    Mapping quantitative trait loci regions associated with Marek’s disease on chicken autosomes by means of selective DNA pooling by Ehud Lipkin, Jacqueline Smith, Morris Soller, David W. Burt, Janet E. Fulton

    Published 2024-12-01
    “…Distribution of P and LD values were used to assess the QTLR causative elements. Allele substitution effects were calculated based on both pooled SNP microarray genotypes, and individual genotypes of QTLRs markers. …”
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    Identification of three small nucleolar RNAs (snoRNAs) as potential prognostic markers in diffuse large B‐cell lymphoma by Mei‐wei Li, Feng‐xiang Huang, Zu‐cheng Xie, Hao‐yuan Hong, Qing‐yuan Xu, Zhi‐gang Peng

    Published 2023-02-01
    “…Results Twelve prognosis‐correlated snoRNAs were selected from the DLBCL patient cohort of microarray profiles, and a three‐snoRNA signature consisting of SNORD1A, SNORA60, and SNORA66 was constructed. …”
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    Article
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    Effects of oxidized phospholipids on gene expression in RAW 264.7 macrophages: a microarray study. by Daniel Koller, Hubert Hackl, Juliane Gertrude Bogner-Strauß, Albin Hermetter

    Published 2014-01-01
    “…In this study we present the effects of 1-palmitoyl-2-glutaroyl-sn-glycero-3-phosphocholine (PGPC) and 1-palmitoyl-2-(5-oxovaleroyl)-sn-glycero-3-phosphocholine (POVPC) on gene expression in RAW 264.7 macrophages using cDNA microarrays. …”
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    Genetic feature selection algorithm as an efficient glioma grade classifier by Ting-Han Lin, Hung-Yi Lin

    Published 2025-05-01
    “…The current study proposes a heuristic feature selection algorithm that identifies subsets of genes to almost perfectly classify glioma grades. …”
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    CFS-MOES Ensemble Model on Metaheuristic Search-Based Feature Selection by Santosini Bhutia, Bichitrananda Patra, Mitrabinda Ray

    Published 2024-01-01
    “…The application of several classification and feature selection methods on microarray gene expression datasets helps learn models that are able to predict a given disease. …”
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    Article
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    A Highly Discriminative Hybrid Feature Selection Algorithm for Cancer Diagnosis by Tarneem Elemam, Mohamed Elshrkawey

    Published 2022-01-01
    “…The algorithm comprises a two-stage hybrid feature selection. In the first stage, an overall ranker is initiated to combine the results of three filter-based feature evaluation methods, namely, chi-squared, F-statistic, and mutual information (MI). …”
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    Microarray analysis of the effects of Acthar Gel versus methylprednisolone in a model of focal segmental glomerulosclerosis in female rats by Kyle Hayes, Dale Wright

    Published 2025-04-01
    “…On Day 56, animals were sacrificed, and RNA samples of kidney cortex tissue were analyzed using microarrays. Compared with control, Acthar significantly decreased the expression of more genes related to inflammation, immune function, and fibrosis than MP. …”
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