In his papers, Raphael Gottardo integrates diverse fields, such as Gene and Phenotype. Raphael Gottardo performs integrative study on Genetics and Computational biology in his works. In his works, Raphael Gottardo conducts interdisciplinary research on Computational biology and Gene. Borrowing concepts from Antibody, Raphael Gottardo weaves in ideas under Immunology. In his works, he performs multidisciplinary study on Data mining and Cluster analysis. The study of Cluster analysis is intertwined with the study of Artificial intelligence in a number of ways. In his papers, Raphael Gottardo integrates diverse fields, such as Artificial intelligence and Data mining. His Biochemistry study frequently involves adjacent topics like Bioconductor. His work on Bioconductor is being expanded to include thematically relevant topics such as Biochemistry.
In the field of Gene expression and Bioconductor Raphael Gottardo studies Gene. His Immune system research is covered under the topics of T cell and CD8. Raphael Gottardo undertakes multidisciplinary studies into T cell and Immune system in his work. With his scientific publications, his incorporates both Immunology and Antigen. Genetics and Computational biology are two areas of study in which he engages in interdisciplinary work. He conducts interdisciplinary study in the fields of Computational biology and Genetics through his works. His Virology study frequently draws connections between related disciplines such as Vaccination. His research ties Virology and Vaccination together. While working in this field, Raphael Gottardo studies both Artificial intelligence and Data mining.
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Orchestrating high-throughput genomic analysis with Bioconductor
Wolfgang Huber;Vincent J Carey;Robert Gentleman;Simon Anders.
Nature Methods (2015)
Integrated analysis of multimodal single-cell data
Yuhan Hao;Stephanie Hao;Erica Andersen-Nissen;William M. Mauck.
MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data
Greg Finak;Andrew McDavid;Masanao Yajima;Jingyuan Deng.
Genome Biology (2015)
Critical assessment of automated flow cytometry data analysis techniques
Nima Aghaeepour;Greg Finak.
Nature Methods (2013)
Model-based analysis of tiling-arrays for ChIP-chip
W. Evan Johnson;Wei Li;Clifford A. Meyer;Raphael Gottardo.
Proceedings of the National Academy of Sciences of the United States of America (2006)
Exosomes in human semen carry a distinctive repertoire of small non-coding RNAs with potential regulatory functions
Lucia Vojtech;Sangsoon Woo;Sean Hughes;Claire Levy.
Nucleic Acids Research (2014)
Combining Mixture Components for Clustering
Jean Patrick Baudry;Adrian E. Raftery;Gilles Celeux;Kenneth Lo.
Journal of Computational and Graphical Statistics (2010)
Automated gating of flow cytometry data via robust model-based clustering.
Kenneth Lo;Ryan Remy Brinkman;Raphael Gottardo.
Cytometry Part A (2008)
Data exploration, quality control and testing in single-cell qPCR-based gene expression experiments
Andrew McDavid;Greg Finak;Pratip K. Chattopadyay;Maria Dominguez.
Multi-Omics Resolves a Sharp Disease-State Shift between Mild and Moderate COVID-19.
Yapeng Su;Daniel Chen;Dan Yuan;Christopher Lausted.
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