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Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics
At the crossroads between statistics and machine learning, probabilistic graphical models (PGMs) provide a powerful formal framework to model complex data. An expanding volume of biological data of various types, the so-called 'omics', is in need of accurate and efficient methods for modelling and PGMs are expected to have a prominent role to play.
Biomedical Engineering Systems and Technologies
This book constitutes the thoroughly refereed post-conference proceedings of the 5th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2012, held in Vilamoura, Portugal, in February 2012. The 26 revised full papers presented together with one invited lecture were carefully reviewed and selected from a total of 522 submissions. The papers cover a wide range of topics and are organized in four general topical sections on biomedical electronics and devices; bioinformatics models, methods and algorithms; bio-inspired systems and signal processing; health informatics.
Nuclear Architecture and Dynamics
Nuclear Architecture and Dynamics provides a definitive resource for (bio)physicists and molecular and cellular biologists whose research involves an understanding of the organization of the genome and the mechanisms of its proper reading, maintenance, and replication by the cell. This book brings together the biochemical and physical characteristics of genome organization, providing a relevant framework in which to interpret the control of gene expression and cell differentiation. It includes work from a group of international experts, including biologists, physicists, mathematicians, and bioinformaticians who have come together for a comprehensive presentation of the current developments i...
Algebraic Statistics
Algebraic statistics uses tools from algebraic geometry, commutative algebra, combinatorics, and their computational sides to address problems in statistics and its applications. The starting point for this connection is the observation that many statistical models are semialgebraic sets. The algebra/statistics connection is now over twenty years old, and this book presents the first broad introductory treatment of the subject. Along with background material in probability, algebra, and statistics, this book covers a range of topics in algebraic statistics including algebraic exponential families, likelihood inference, Fisher's exact test, bounds on entries of contingency tables, design of experiments, identifiability of hidden variable models, phylogenetic models, and model selection. With numerous examples, references, and over 150 exercises, this book is suitable for both classroom use and independent study.
Proceedings of COMPSTAT'2010
Proceedings of the 19th international symposium on computational statistics, held in Paris august 22-27, 2010.Together with 3 keynote talks, there were 14 invited sessions and more than 100 peer-reviewed contributed communications.
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