G
Georg K. Gerber
Researcher at Brigham and Women's Hospital
Publications - 69
Citations - 9258
Georg K. Gerber is an academic researcher from Brigham and Women's Hospital. The author has contributed to research in topics: Microbiome & Population. The author has an hindex of 28, co-authored 66 publications receiving 7919 citations. Previous affiliations of Georg K. Gerber include Harvard University & Massachusetts Institute of Technology.
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Journal ArticleDOI
Author Correction: Microbiota therapy acts via a regulatory T cell MyD88/RORγt pathway to suppress food allergy.
Azza Abdel-Gadir,Azza Abdel-Gadir,Emmanuel Stephen-Victor,Emmanuel Stephen-Victor,Georg K. Gerber,Magali Noval Rivas,Sean-Jiun Wang,Sean-Jiun Wang,Hani Harb,Hani Harb,Leighanne Wang,Ning Li,Elena Crestani,Elena Crestani,Sara Spielman,William Secor,Heather Biehl,Nicholas DiBenedetto,Xiaoxi Dong,Dale T. Umetsu,Lynn Bry,Rima Rachid,Rima Rachid,Talal A. Chatila,Talal A. Chatila +24 more
TL;DR: An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Improving microbial fitness in the mammalian gut by in vivo temporal functional metagenomics
Stephanie J. Yaung,Stephanie J. Yaung,Stephanie J. Yaung,Luxue Deng,Ning Li,Jonathan L. Braff,George M. Church,George M. Church,Lynn Bry,Harris H. Wang,Harris H. Wang,Georg K. Gerber +11 more
TL;DR: TFUMseq is presented, a platform to functionally mine bacterial genomes for genes that contribute to fitness of commensal bacteria in vivo by using metagenomic DNA to construct large‐scale heterologous expression libraries that are tracked over time by deep sequencing and computational methods.
Posted ContentDOI
MITRE: predicting host status from microbiota time-series data
TL;DR: Microbiome Interpretable Temporal Rule Engine (MITRE), the first machine learning method specifically designed for predicting host status from microbiome time-series data, is presented, providing a powerful new tool enabling discovery of biologically interpretable relationships between microbiome and human host.
Microbial dynamics inference at ecosystem-scale
Travis E. Gibson,Younhun Kim,Sawal Acharya,David E. Kaplan,Nicholas DiBenedetto,Richard Lavin,Bonnie Berger,Jessica R. Allegretti,Lynn Bry,Georg K. Gerber +9 more
TL;DR: The Microbial Dynamical Systems Inference Engine 2 (MDSINE2) as discussed by the authors infers compact and interpretable ecosystems-scale dynamical systems models from microbiome time-series data.