Handbook of Statistical Bioinformatics

Henry Horng-Shing Lu

ISBN 10: 3662659018 / ISBN 13: 9783662659014

Published by Springer Berlin Heidelberg, Springer Berlin Heidelberg Dez 2022, 2022

About this Item

Neuware -Now in its second edition, this handbook collects authoritative contributions on modern methods and tools in statistical bioinformatics with a focus on the interface between computational statistics and cutting-edge developments in computational biology. The three parts of the book cover statistical methods for single-cell analysis, network analysis, and systems biology, with contributions by leading experts addressing key topics in probabilistic and statistical modeling and the analysis of massive data sets generated by modern biotechnology. This handbook will serve as a useful reference source for students, researchers and practitioners in statistics, computer science and biological and biomedical research, who are interested in the latest developments in computational statistics as applied to computational biology.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 420 pp. Englisch. Seller Inventory # 9783662659014

Bibliographic Details

Title: Handbook of Statistical Bioinformatics

Publisher: Springer Berlin Heidelberg, Springer Berlin Heidelberg Dez 2022

Publication Date: 2022

Binding: Buch

Condition: Neu

Edition: 2nd Edition

About this title

Synopsis:

Now in its second edition, this handbook collects authoritative contributions on modern methods and tools in statistical bioinformatics with a focus on the interface between computational statistics and cutting-edge developments in computational biology. The three parts of the book cover statistical methods for single-cell analysis, network analysis, and systems biology, with contributions by leading experts addressing key topics in probabilistic and statistical modeling and the analysis of massive data sets generated by modern biotechnology. This handbook will serve as a useful reference source for students, researchers and practitioners in statistics, computer science and biological and biomedical research, who are interested in the latest developments in computational statistics as applied to computational biology.

About the Author:

Henry Horng-Shing Lu is a Professor at National Chiao Tung University (NCTU), which is merged with National Yang Ming Chiao Tung University (NYCU). He has served as the Vice President for Academic Affairs and Dean for College of Science in NCTU. He is an Elected Member of the International Statistical Institute and Principal Fellow of the High Education Academy. His research interests include statistics, data science, machine learning, image science, biomedical studies and industrial applications.

Bernhard Schölkopf is a member of the Max Planck Society and the Director of the Department of Empirical Inference at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. He is also an Honorary Professor of Machine Learning at the Technical University Berlin. His scientific interests are in the field of machine learning and inference from empirical data, in particular, in machine learning methods for extracting statistical and causal regularities.

Martin T. Wells is the Charles A. Alexander Professor of Statistical Sciences at Cornell University in Ithaca, NY, USA. He is also a Professor of Biostatistics and Epidemiology at Weill Medical School, an Elected Member of the Cornell Law School Faculty, as well as the Director of Research in the School of Industrial and Labor Relations. His research fields and topics include biostatistics, Bayesian statistics, decision theory, epidemiology, high-dimensional data analysis, statistical bioinformatics, and empirical legal studies.

Hongyu Zhao is the Ira V. Hiscock Professor of Biostatistics and Professor of Statistics and Data Science and Genetics at Yale University, New Haven, CT, USA where he also serves as the Director of the Center for Statistical Genomics and Proteomics. His research interests include statistical genomics, computational biology, statistical proteomics, risk prediction, high dimensional data analysis, and network modeling and inference.