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  1. 1
    ENmix: a novel background correction method for Illumina HumanMethylation450 BeadChip

    ENmix: a novel background correction method for Illumina HumanMethylation450 BeadChip mā Zongli Xu, Liang Niu, Leping Li, Jack A. Taylor

    I whakaputaina 2015
    Whiwhi kuputuhi katoa Whiwhi kuputuhi katoa
    Artigo
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  2. 2
    <scp>MicroRNA</scp> levels are modulated in <i><scp>A</scp>edes aegypti</i> after exposure to <scp>D</scp>engue‐2

    <scp>MicroRNA</scp> levels are modulated in <i><scp>A</scp>edes aegypti</i> after exposure to <scp>D</scp>engue‐2 mā Corey L. Campbell, Tabitha A. Harrison, Ann M. Hess, G. D. Ebel

    I whakaputaina 2013
    Whiwhi kuputuhi katoa
    Artigo
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  3. 3
    Chromatin segmentation based on a probabilistic model for read counts explains a large portion of the epigenome

    Chromatin segmentation based on a probabilistic model for read counts explains a large portion of the epigenome mā Alessandro Mammana, Ho‐Ryun Chung

    I whakaputaina 2015
    Whiwhi kuputuhi katoa Whiwhi kuputuhi katoa
    Artigo
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  4. 4
    How to build personalized multi-omics comorbidity profiles

    How to build personalized multi-omics comorbidity profiles mā Mohammad Ali Moni, Píetro Lió

    I whakaputaina 2015
    Whiwhi kuputuhi katoa Whiwhi kuputuhi katoa
    Artigo
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  5. 5
    Spatial normalization of array-CGH data

    Spatial normalization of array-CGH data mā Pierre Neuvial, Philippe Hupé, Isabel Martínez Brito, Stéphane Liva, Élodie Manié, Caroline Brennetot, François Radvanyi, Alain Aurias, Emmanuel Barillot

    I whakaputaina 2006
    Whiwhi kuputuhi katoa Whiwhi kuputuhi katoa
    Artigo
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  6. 6
    Detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicates

    Detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicates mā Hao Wu, Tianlei Xu, Hao Feng, Li Chen, Ben Li, Bing Yao, Zhaohui Qin, Peng Jin, Karen N. Conneely

    I whakaputaina 2015
    Whiwhi kuputuhi katoa Whiwhi kuputuhi katoa
    Artigo
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  7. 7
    Empirical array quality weights in the analysis of microarray data

    Empirical array quality weights in the analysis of microarray data mā Matthew E. Ritchie, Dileepa Diyagama, Jody Neilson, Ryan K. van Laar, Alexander Dobrovic, Andrew J. Holloway, Gordon K. Smyth

    I whakaputaina 2006
    Whiwhi kuputuhi katoa Whiwhi kuputuhi katoa
    Artigo
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  8. 8
    The Analysis of Gene Expression Data: Methods and Software

    The Analysis of Gene Expression Data: Methods and Software mā Thomas Boyle

    I whakaputaina 2005
    Whiwhi kuputuhi katoa
    Artigo
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  9. 9
    A guide to genome‐wide association analysis and post‐analytic interrogation

    A guide to genome‐wide association analysis and post‐analytic interrogation mā Eric Reed, Sara Núñez, David Kulp, Jing Qian, Muredach P. Reilly, Andrea S. Foulkes

    I whakaputaina 2015
    Whiwhi kuputuhi katoa Whiwhi kuputuhi katoa
    Artigo
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  10. 10
    Analysis of cell-based RNAi screens

    Analysis of cell-based RNAi screens mā Michael Boutros, Lı́gia P. Brás, Wolfgang Huber

    I whakaputaina 2006
    Whiwhi kuputuhi katoa Whiwhi kuputuhi katoa
    Artigo
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  11. 11
    EMDomics: a robust and powerful method for the identification of genes differentially expressed between heterogeneous classes

    EMDomics: a robust and powerful method for the identification of genes differentially expressed between heterogeneous classes mā Sheida Nabavi, Daniel Schmolze, Mayinuer Maitituoheti, Sadhika Malladi, Andrew H. Beck

    I whakaputaina 2015
    Whiwhi kuputuhi katoa
    Artigo
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  12. 12
    ToPASeq: an R package for topology-based pathway analysis of microarray and RNA-Seq data

    ToPASeq: an R package for topology-based pathway analysis of microarray and RNA-Seq data mā Ivana Ihnatová, Eva Budínská

    I whakaputaina 2015
    Whiwhi kuputuhi katoa Whiwhi kuputuhi katoa
    Artigo
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  13. 13
    diffHic: a Bioconductor package to detect differential genomic interactions in Hi-C data

    diffHic: a Bioconductor package to detect differential genomic interactions in Hi-C data mā Aaron T. L. Lun, Gordon K. Smyth

    I whakaputaina 2015
    Whiwhi kuputuhi katoa Whiwhi kuputuhi katoa
    Artigo
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  14. 14
    quantro: a data-driven approach to guide the choice of an appropriate normalization method

    quantro: a data-driven approach to guide the choice of an appropriate normalization method mā Stephanie C. Hicks, Rafael A. Irizarry

    I whakaputaina 2015
    Whiwhi kuputuhi katoa Whiwhi kuputuhi katoa
    Artigo
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  15. 15
    Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation

    Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation mā Michael I. Love, John B. Hogenesch, Rafael A. Irizarry

    I whakaputaina 2016
    Whiwhi kuputuhi katoa
    Artigo
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  16. 16
    Interpretation of multiple probe sets mapping to the same gene in Affymetrix GeneChips

    Interpretation of multiple probe sets mapping to the same gene in Affymetrix GeneChips mā Maria A. Stalteri, Andrew Harrison

    I whakaputaina 2007
    Whiwhi kuputuhi katoa Whiwhi kuputuhi katoa
    Artigo
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  17. 17
    CompGO: an R package for comparing and visualizing Gene Ontology enrichment differences between DNA binding experiments

    CompGO: an R package for comparing and visualizing Gene Ontology enrichment differences between DNA binding experiments mā Ashley J. Waardenberg, Samuel D. Bassett, Romaric Bouveret, Richard P. Harvey

    I whakaputaina 2015
    Whiwhi kuputuhi katoa Whiwhi kuputuhi katoa
    Artigo
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  18. 18
    INSPEcT: a computational tool to infer mRNA synthesis, processing and degradation dynamics from RNA- and 4sU-seq time course experiments

    INSPEcT: a computational tool to infer mRNA synthesis, processing and degradation dynamics from RNA- and 4sU-seq time course experiments mā Stefano de Pretis, Theresia Kreß, Marco J. Morelli, Giorgio Melloni, Laura Riva, Bruno Amati, Mattia Pelizzola

    I whakaputaina 2015
    Whiwhi kuputuhi katoa Whiwhi kuputuhi katoa
    Artigo
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  19. 19
    DiffVar: a new method for detecting differential variability with application to methylation in cancer and aging

    DiffVar: a new method for detecting differential variability with application to methylation in cancer and aging mā Belinda Phipson, Alicia Oshlack

    I whakaputaina 2014
    Whiwhi kuputuhi katoa Whiwhi kuputuhi katoa
    Artigo
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  20. 20
    Exploring Massive, Genome Scale Datasets with the GenometriCorr Package

    Exploring Massive, Genome Scale Datasets with the GenometriCorr Package mā Alexander V. Favorov, Loris Mularoni, Leslie Cope, Yulia A. Medvedeva, Andrey A. Mironov, Vsevolod J. Makeev, Sarah J. Wheelan

    I whakaputaina 2012
    Whiwhi kuputuhi katoa Whiwhi kuputuhi katoa
    Artigo
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