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Titlebook: Human Cell Transformation; Advances in Cell Mod Johng S. Rhim,Anatoly Dritschilo,Richard Kremer Book 2019 The Editor(s) (if applicable) and

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书目名称Human Cell Transformation
副标题Advances in Cell Mod
编辑Johng S. Rhim,Anatoly Dritschilo,Richard Kremer
视频video
概述Reflects worldwide expertise and experience.Makes a June 2018 conference‘s paper and research accessible to a worldwide audience.Presents cutting edge information on a very hot and timely topic
丛书名称Advances in Experimental Medicine and Biology
图书封面Titlebook: Human Cell Transformation; Advances in Cell Mod Johng S. Rhim,Anatoly Dritschilo,Richard Kremer Book 2019 The Editor(s) (if applicable) and
描述This book, part contributed volume, part proceedings, discusses state-of-the-art advances on human cell transformation in cell models for the study of cancer and aging. Several of the chapters are from the Human Cell Transformation: Advances in Cell Models for the Study of Cancer and Aging conference that was held in June 2018 at McGill University. The authors represent international expertise on a wide variety of topics ranging from different types of cancer (prostate, bone, breast, etc.) to tumor microenvironment, tumor progression, homogeneity, and possible therapies and treatments..
出版日期Book 2019
关键词cancer cell; cancer and aging; cell transformation; cancer immunotherapy; heterogeneity
版次1
doihttps://doi.org/10.1007/978-3-030-22254-3
isbn_softcover978-3-030-22256-7
isbn_ebook978-3-030-22254-3Series ISSN 0065-2598 Series E-ISSN 2214-8019
issn_series 0065-2598
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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c treatment of Bayesian nonparametric methods and the theory behind them. While the book is of special interest to Bayesians, it will also appeal to statisticians in general because Bayesian nonparametrics offers a whole continuous spectrum of robust alternatives to purely parametric and purely nonp
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Jason R. Pitarresi,Anil K. Rustgit of Bayesian nonparametric methods and the theory behind them. While the book is of special interest to Bayesians, it will also appeal to statisticians in general because Bayesian nonparametrics offers a whole continuous spectrum of robust alternatives to purely parametric and purely nonparametric
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J. Russo,Y. Suc treatment of Bayesian nonparametric methods and the theory behind them. While the book is of special interest to Bayesians, it will also appeal to statisticians in general because Bayesian nonparametrics offers a whole continuous spectrum of robust alternatives to purely parametric and purely nonp
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