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human microarray platform affymetrix human genome u133a array  (Thermo Fisher)


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    Thermo Fisher human microarray platform affymetrix human genome u133a array
    Performance of imputation methods for microarray data . Boxplots showing the performance of the six imputation methods ( SampleLASSO , GeneGAN , GeneDNN , GeneLASSO , SampleKNN , GeneKNN ) across two gene subsets ( A : <t>GPL96-570</t> and B : LINCS), trained and imputed on microarray data. The evaluation metric is NRMSE, with lower values indicating better performance, and the methods are ordered by the median value.
    Human Microarray Platform Affymetrix Human Genome U133a Array, supplied by Thermo Fisher, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/human+microarray+platform+affymetrix+human+genome+u133a+array/pmc07708069-50-17-20?v=Thermo+Fisher
    Average 86 stars, based on 1 article reviews
    human microarray platform affymetrix human genome u133a array - by Bioz Stars, 2026-08
    86/100 stars

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    1) Product Images from "A flexible, interpretable, and accurate approach for imputing the expression of unmeasured genes"

    Article Title: A flexible, interpretable, and accurate approach for imputing the expression of unmeasured genes

    Journal: Nucleic Acids Research

    doi: 10.1093/nar/gkaa881

    Performance of imputation methods for microarray data . Boxplots showing the performance of the six imputation methods ( SampleLASSO , GeneGAN , GeneDNN , GeneLASSO , SampleKNN , GeneKNN ) across two gene subsets ( A : GPL96-570 and B : LINCS), trained and imputed on microarray data. The evaluation metric is NRMSE, with lower values indicating better performance, and the methods are ordered by the median value.
    Figure Legend Snippet: Performance of imputation methods for microarray data . Boxplots showing the performance of the six imputation methods ( SampleLASSO , GeneGAN , GeneDNN , GeneLASSO , SampleKNN , GeneKNN ) across two gene subsets ( A : GPL96-570 and B : LINCS), trained and imputed on microarray data. The evaluation metric is NRMSE, with lower values indicating better performance, and the methods are ordered by the median value.

    Techniques Used: Microarray

    Performance of imputation methods for cross-technology imputation . Boxplots showing the performance of the six imputation methods ( SampleLASSO , GeneGAN , GeneDNN , GeneLASSO , SampleKNN , GeneKNN ) across two gene subsets ( A : GPL96-570 and B : LINCS) using RNA-seq data to impute microarray data. The evaluation metric is NRMSE, with lower values indicating better performance, and the methods are ordered by the median value.
    Figure Legend Snippet: Performance of imputation methods for cross-technology imputation . Boxplots showing the performance of the six imputation methods ( SampleLASSO , GeneGAN , GeneDNN , GeneLASSO , SampleKNN , GeneKNN ) across two gene subsets ( A : GPL96-570 and B : LINCS) using RNA-seq data to impute microarray data. The evaluation metric is NRMSE, with lower values indicating better performance, and the methods are ordered by the median value.

    Techniques Used: RNA Sequencing Assay, Microarray

    Performance of imputation methods for RNA-seq data . Boxplots showing the performance of the six imputation methods ( SampleLASSO , GeneGAN , GeneDNN , GeneLASSO , SampleKNN , GeneKNN ) across two gene subsets ( A : GPL96-570 and B : LINCS) using RNA-seq data to impute RNA-seq data. The evaluation metric is NRMSE, with lower values indicating better performance, and the methods are ordered by the median value.
    Figure Legend Snippet: Performance of imputation methods for RNA-seq data . Boxplots showing the performance of the six imputation methods ( SampleLASSO , GeneGAN , GeneDNN , GeneLASSO , SampleKNN , GeneKNN ) across two gene subsets ( A : GPL96-570 and B : LINCS) using RNA-seq data to impute RNA-seq data. The evaluation metric is NRMSE, with lower values indicating better performance, and the methods are ordered by the median value.

    Techniques Used: RNA Sequencing Assay



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    human microarray platform affymetrix human genome u133a array  (Thermo Fisher)
    86
    Thermo Fisher human microarray platform affymetrix human genome u133a array
    Performance of imputation methods for microarray data . Boxplots showing the performance of the six imputation methods ( SampleLASSO , GeneGAN , GeneDNN , GeneLASSO , SampleKNN , GeneKNN ) across two gene subsets ( A : <t>GPL96-570</t> and B : LINCS), trained and imputed on microarray data. The evaluation metric is NRMSE, with lower values indicating better performance, and the methods are ordered by the median value.
    Human Microarray Platform Affymetrix Human Genome U133a Array, supplied by Thermo Fisher, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/human+microarray+platform+affymetrix+human+genome+u133a+array/pmc07708069-50-17-20?v=Thermo+Fisher
    Average 86 stars, based on 1 article reviews
    human microarray platform affymetrix human genome u133a array - by Bioz Stars, 2026-08
    86/100 stars
    		  Buy from Supplier

    Image Search Results


    Performance of imputation methods for microarray data . Boxplots showing the performance of the six imputation methods ( SampleLASSO , GeneGAN , GeneDNN , GeneLASSO , SampleKNN , GeneKNN ) across two gene subsets ( A : GPL96-570 and B : LINCS), trained and imputed on microarray data. The evaluation metric is NRMSE, with lower values indicating better performance, and the methods are ordered by the median value.

    Journal: Nucleic Acids Research

    Article Title: A flexible, interpretable, and accurate approach for imputing the expression of unmeasured genes

    doi: 10.1093/nar/gkaa881

    Figure Lengend Snippet: Performance of imputation methods for microarray data . Boxplots showing the performance of the six imputation methods ( SampleLASSO , GeneGAN , GeneDNN , GeneLASSO , SampleKNN , GeneKNN ) across two gene subsets ( A : GPL96-570 and B : LINCS), trained and imputed on microarray data. The evaluation metric is NRMSE, with lower values indicating better performance, and the methods are ordered by the median value.

    Article Snippet: This scenario presents itself in the problem of using the 11 678 genes measured in the older human microarray platform Affymetrix Human Genome U133A Array (i.e. GPL96) to then impute the expression of an additional 5 277 genes that are only present in the newer genome-scale platform Affymetrix Human Genome U133 Plus 2.0 Array (i.e. GPL570) ( ).

    Techniques: Microarray

    Performance of imputation methods for cross-technology imputation . Boxplots showing the performance of the six imputation methods ( SampleLASSO , GeneGAN , GeneDNN , GeneLASSO , SampleKNN , GeneKNN ) across two gene subsets ( A : GPL96-570 and B : LINCS) using RNA-seq data to impute microarray data. The evaluation metric is NRMSE, with lower values indicating better performance, and the methods are ordered by the median value.

    Journal: Nucleic Acids Research

    Article Title: A flexible, interpretable, and accurate approach for imputing the expression of unmeasured genes

    doi: 10.1093/nar/gkaa881

    Figure Lengend Snippet: Performance of imputation methods for cross-technology imputation . Boxplots showing the performance of the six imputation methods ( SampleLASSO , GeneGAN , GeneDNN , GeneLASSO , SampleKNN , GeneKNN ) across two gene subsets ( A : GPL96-570 and B : LINCS) using RNA-seq data to impute microarray data. The evaluation metric is NRMSE, with lower values indicating better performance, and the methods are ordered by the median value.

    Article Snippet: This scenario presents itself in the problem of using the 11 678 genes measured in the older human microarray platform Affymetrix Human Genome U133A Array (i.e. GPL96) to then impute the expression of an additional 5 277 genes that are only present in the newer genome-scale platform Affymetrix Human Genome U133 Plus 2.0 Array (i.e. GPL570) ( ).

    Techniques: RNA Sequencing Assay, Microarray

    Performance of imputation methods for RNA-seq data . Boxplots showing the performance of the six imputation methods ( SampleLASSO , GeneGAN , GeneDNN , GeneLASSO , SampleKNN , GeneKNN ) across two gene subsets ( A : GPL96-570 and B : LINCS) using RNA-seq data to impute RNA-seq data. The evaluation metric is NRMSE, with lower values indicating better performance, and the methods are ordered by the median value.

    Journal: Nucleic Acids Research

    Article Title: A flexible, interpretable, and accurate approach for imputing the expression of unmeasured genes

    doi: 10.1093/nar/gkaa881

    Figure Lengend Snippet: Performance of imputation methods for RNA-seq data . Boxplots showing the performance of the six imputation methods ( SampleLASSO , GeneGAN , GeneDNN , GeneLASSO , SampleKNN , GeneKNN ) across two gene subsets ( A : GPL96-570 and B : LINCS) using RNA-seq data to impute RNA-seq data. The evaluation metric is NRMSE, with lower values indicating better performance, and the methods are ordered by the median value.

    Article Snippet: This scenario presents itself in the problem of using the 11 678 genes measured in the older human microarray platform Affymetrix Human Genome U133A Array (i.e. GPL96) to then impute the expression of an additional 5 277 genes that are only present in the newer genome-scale platform Affymetrix Human Genome U133 Plus 2.0 Array (i.e. GPL570) ( ).

    Techniques: RNA Sequencing Assay