7 research outputs found

    Gene network analysis in “Weak” and “Strong” vectors.

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    <p>A: Selection of STAT-1 related genes derived from Sig1 of the RFM model were targeted on Ingenuity Pathway Analysis (IPA). B: CH25H, a gene selected in one of the other 26 important signatures of the model, was targeted as the key gene on IPA. The grow functionality was used to display all known direct and indirect interactions with CH25H, except miRNA. The biological interactions of CH25H are displayed on A (black arrows). Colors depend on statistical analyses (red: upregulated, green: downregulated) performed on rAd_1, AP205_1, MPY_3bis and BCG_2 vector datasets; color intensities were set to be in the same range in all experiments.</p

    Predictions of human PBMC transcriptome data derived 6, 24 and 72 hours after vaccination by MRKAd5/HIV published in [25].

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    <p>Predictions of human PBMC transcriptome data derived 6, 24 and 72 hours after vaccination by MRKAd5/HIV published in [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004801#pcbi.1004801.ref025" target="_blank">25</a>].</p

    Modelling strategy.

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    <p>(i) For each pre-processed dataset, composed of microarray measures for mice injected with vector 1 (V1.1, V1.2 …) and control (C1.1, C1.2 …), one hundred datasets were created by bootstrapping samples among V and C. (ii) Ranked gene lists, according to the eBayes statistical comparison of vector and control conditions, were generated. (iii) Potential signatures were tested for enrichment on each of the 100 ranked gene lists by GSEA. The resulting NES matrix was then used to build the random forest model.</p
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