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Gene expression of neuroblastoma samples was quantified by RNA sequencing as well as by microarray analyses in order to understand the neuroblastoma transcriptome and predict clinical endpoints. Collectively, our study reveals an unprecedented complexity of the neuroblastoma transcriptome, and provides guidelines for the development of gene expression-based predictive classifiers using high-throughput technologies. The neuroblastoma cohort was randomly divided into training and validation sets, and predictive models on six clinical endpoints were generated and evaluated. Not logged in Login. We sought to systematically evaluate the capability of RNA deep-sequencing RNA-Seq -based classification for clinical endpoint prediction in comparison to microarray-based ones. Series GSE

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    in differential gene expression experiments with external spike-in RNA control. 15; Sp. p. ; Gaz. vol. ; p.

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    Gaz. vol. ; p. Ladd, Sherman W. everly, Mass., assignor to United Shoe Machiner ompany, Laney, Geo. K., et al.
    The neuroblastoma cohort was randomly divided into training and validation sets, and predictive models on six clinical endpoints were generated and evaluated.

    Due to patient privacy issues the raw data was not submitted. GEO help: Mouse over screen elements for information. SOFT formatted family file s. Gene expression of neuroblastoma samples was quantified by RNA sequencing as well as by microarray analyses in order to understand the neuroblastoma transcriptome and predict clinical endpoints.

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    RNA-Seq reveals an unprecedented complexity of the neuroblastoma transcriptome and is suitable for clinical endpoint prediction [RNA-Seq]. Homo sapiens.

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    We generated gene expression profiles from primary neuroblastomas using RNA-Seq and microarrays. Homo sapiens. While prediction performances did not differ considerably between the two technical platforms, the RNA-Seq data processing pipelines, or feature levels i.

    MINiML formatted family file s. The neuroblastoma cohort was randomly divided into training and validation sets, and predictive models on six clinical endpoints were generated and evaluated.

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    Table 3.
    Gene expression of neuroblastoma samples was quantified by RNA sequencing as well as by microarray analyses in order to understand the neuroblastoma transcriptome and predict clinical endpoints. Collectively, our study reveals an unprecedented complexity of the neuroblastoma transcriptome, and provides guidelines for the development of gene expression-based predictive classifiers using high-throughput technologies.

    SOFT formatted family file s. While prediction performances did not differ considerably between the two technical platforms, the RNA-Seq data processing pipelines, or feature levels i.

    Due to patient privacy issues the raw data was not submitted.

    images geo sp 160 w
    Geo sp 160 w
    We generated gene expression profiles from primary neuroblastomas using RNA-Seq and microarrays.

    RNA-Seq reveals an unprecedented complexity of the neuroblastoma transcriptome and is suitable for clinical endpoint prediction [RNA-Seq]. The neuroblastoma cohort was randomly divided into training and validation sets, and predictive models on six clinical endpoints were generated and evaluated. Series Matrix File s.

    SOFT formatted family file s.

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    1. We generated gene expression profiles from primary neuroblastomas using RNA-Seq and microarrays. GEO help: Mouse over screen elements for information.

    2. The neuroblastoma cohort was randomly divided into training and validation sets, and predictive models on six clinical endpoints were generated and evaluated.