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244 k (g4502a) microarray data  (Agilent technologies)


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    Agilent technologies 244 k (g4502a) microarray data
    244 K (G4502a) Microarray Data, supplied by Agilent technologies, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/244+k+microarray+data/pm32594352-232-7-9
    Average 90 stars, based on 1 article reviews
    244 k (g4502a) microarray data - by Bioz Stars, 2026-09
    90/100 stars

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    Related Articles

    Expressing:

    Article Title: Nuclear Respiratory Factor 1 (NRF1) Transcriptional Activity-Driven Gene Signature Association with Severity of Astrocytoma and Poor Prognosis of Glioblastoma.
    Article Snippet: Despite tremendous progress in understanding the pathobiology of astrocytoma, major gaps remain in our knowledge of the molecular basis underlying the aggressiveness of high-grade astrocytoma (glioblastoma GBM).. Recently, we and others have shown nuclear respiratory factor 1 (NRF1) transcription factor being highly active in human cancers, but its role in astrocytoma remains unknown.. Therefore, the purpose of this study was to uncover the role of NRF1 in the progression of GBM.

    Microarray:

    Article Title: Nuclear Respiratory Factor 1 (NRF1) Transcriptional Activity-Driven Gene Signature Association with Severity of Astrocytoma and Poor Prognosis of Glioblastoma.
    Article Snippet: Despite tremendous progress in understanding the pathobiology of astrocytoma, major gaps remain in our knowledge of the molecular basis underlying the aggressiveness of high-grade astrocytoma (glioblastoma GBM).. Recently, we and others have shown nuclear respiratory factor 1 (NRF1) transcription factor being highly active in human cancers, but its role in astrocytoma remains unknown.. Therefore, the purpose of this study was to uncover the role of NRF1 in the progression of GBM.

    Produced:

    Article Title: Nuclear Respiratory Factor 1 (NRF1) Transcriptional Activity-Driven Gene Signature Association with Severity of Astrocytoma and Poor Prognosis of Glioblastoma.
    Article Snippet: Despite tremendous progress in understanding the pathobiology of astrocytoma, major gaps remain in our knowledge of the molecular basis underlying the aggressiveness of high-grade astrocytoma (glioblastoma GBM).. Recently, we and others have shown nuclear respiratory factor 1 (NRF1) transcription factor being highly active in human cancers, but its role in astrocytoma remains unknown.. Therefore, the purpose of this study was to uncover the role of NRF1 in the progression of GBM.



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    244 k microarray data  (Agilent technologies)
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    a TCGA matched samples (520 from BRCA, 150 from GBM) run on both <t>microarray</t> and RNA-seq were split into a training set (2/3) and test set (1/3). b RNA-seq samples were titrated into each training set, 10% at a time (0–100%), resulting in eleven training sets for each normalization method. Each RNA-seq sample replaces its matched microarray sample. Cross-platform normalization methods were applied to each training set independently. c We used three supervised algorithms to train classifiers (molecular subtype and mutation status of TP53 and PIK3CA in both BRCA and GBM) on each training set and tested on the microarray and RNA-seq test sets. The test sets were projected onto and back out of the training set space using unsupervised Principal Components Analysis to obtain reconstructed test sets. The subtype classifiers trained in step 3A were used to predict on the reconstructed test sets. Pathways regulating gene expression were identified using the unsupervised method PLIER.
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    244 k (g4502a) microarray data  (Agilent technologies)
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    Agilent technologies 244 k (g4502a) microarray data
    a TCGA matched samples (520 from BRCA, 150 from GBM) run on both <t>microarray</t> and RNA-seq were split into a training set (2/3) and test set (1/3). b RNA-seq samples were titrated into each training set, 10% at a time (0–100%), resulting in eleven training sets for each normalization method. Each RNA-seq sample replaces its matched microarray sample. Cross-platform normalization methods were applied to each training set independently. c We used three supervised algorithms to train classifiers (molecular subtype and mutation status of TP53 and PIK3CA in both BRCA and GBM) on each training set and tested on the microarray and RNA-seq test sets. The test sets were projected onto and back out of the training set space using unsupervised Principal Components Analysis to obtain reconstructed test sets. The subtype classifiers trained in step 3A were used to predict on the reconstructed test sets. Pathways regulating gene expression were identified using the unsupervised method PLIER.
    244 K (G4502a) Microarray Data, supplied by Agilent technologies, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/244+k+microarray+data/pm32594352-232-7-9
    Average 90 stars, based on 1 article reviews
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    a TCGA matched samples (520 from BRCA, 150 from GBM) run on both microarray and RNA-seq were split into a training set (2/3) and test set (1/3). b RNA-seq samples were titrated into each training set, 10% at a time (0–100%), resulting in eleven training sets for each normalization method. Each RNA-seq sample replaces its matched microarray sample. Cross-platform normalization methods were applied to each training set independently. c We used three supervised algorithms to train classifiers (molecular subtype and mutation status of TP53 and PIK3CA in both BRCA and GBM) on each training set and tested on the microarray and RNA-seq test sets. The test sets were projected onto and back out of the training set space using unsupervised Principal Components Analysis to obtain reconstructed test sets. The subtype classifiers trained in step 3A were used to predict on the reconstructed test sets. Pathways regulating gene expression were identified using the unsupervised method PLIER.

    Journal: Communications Biology

    Article Title: Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously

    doi: 10.1038/s42003-023-04588-6

    Figure Lengend Snippet: a TCGA matched samples (520 from BRCA, 150 from GBM) run on both microarray and RNA-seq were split into a training set (2/3) and test set (1/3). b RNA-seq samples were titrated into each training set, 10% at a time (0–100%), resulting in eleven training sets for each normalization method. Each RNA-seq sample replaces its matched microarray sample. Cross-platform normalization methods were applied to each training set independently. c We used three supervised algorithms to train classifiers (molecular subtype and mutation status of TP53 and PIK3CA in both BRCA and GBM) on each training set and tested on the microarray and RNA-seq test sets. The test sets were projected onto and back out of the training set space using unsupervised Principal Components Analysis to obtain reconstructed test sets. The subtype classifiers trained in step 3A were used to predict on the reconstructed test sets. Pathways regulating gene expression were identified using the unsupervised method PLIER.

    Article Snippet: For BRCA (520 pairs of matched samples), we used log 2 -transformed, lowess normalized Agilent 244 K microarray data and RSEM (RNA-seq by Expectation Maximization) gene-level count RNA-seq data .

    Techniques: Microarray, RNA Sequencing Assay, Mutagenesis, Expressing