Handbook of Statistical Bioinformatics

Lu, Henry Horng-Shing (EDT); Schölkopf, Bernhard (EDT); Zhao, Hongyu (EDT)

ISBN 10: 3642268277 / ISBN 13: 9783642268274

Editore: Springer, 2013

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Dati bibliografici

• Titolo: Handbook of Statistical Bioinformatics
• Editore: Springer
• Data di pubblicazione: 2013
• Lingua: Inglese
• ISBN 10: 3642268277
• ISBN 13: 9783642268274
• Rilegatura: Brossura
• Condizione: As New

Descrizione articolo

Riassunto

Numerous fascinating breakthroughs in biotechnology have generated large volumes and diverse types of high throughput data that demand the development of efficient and appropriate tools in computational statistics integrated with biological knowledge and computational algorithms. This volume collects contributed chapters from leading researchers to survey the many active research topics and promote the visibility of this research area. This volume is intended to provide an introductory and reference book for students and researchers who are interested in the recent developments of computational statistics in computational biology.

Informazioni sull?autore

Henry Horng-Shing Lu is a Professor at the National Chiao Tung University's Institute of Statistics in Taiwan. He is also the Chairman of the University's Interdisciplinary Sciences Degree Program in the College of Science. His research interests are in the field of interdisciplinary studies related to statistics, medical images and bioinformatics.

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

Hongyu Zhao is a Professor of Biostatistics, Statistics, and Genetics at Yale University, where he also serves as the Director of the Center for Statistical Genomics and Protoemics. His research interests include statistical genomics, computational biology, statistical proteomics, risk prediction, high dimensional data analysis, and network modeling and inference.