Probabilistic Modeling in Bioinformatics and Medical Informatics
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Beschreibung
Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, machine learning, and the biological sciences. The first part of this book provides a self-contained introduction to the methodology of Bayesian networks. The following parts demonstrate how these methods are applied in bioinformatics and medical informatics. All three fields - the methodology of probabilistic modeling, bioinformatics, and medical informatics - are evolving very quickly. The text should therefore be seen as an introduction, offering both elementary tutorials as well as more advanced applications and case studies.
There is currently no other book that provides a contemporary survey of probabilistic models in both bio-medicine and bio-informatics An internet site will accompany the book Includes supplementary material: sn.pub/extras
Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, machine learning, and the biological sciences. The first part of this book provides a self-contained introduction to the methodology of Bayesian networks. The following parts demonstrate how these methods are applied in bioinformatics and medical informatics. All three fields - the methodology of probabilistic modelling, bioinformatics, and medical informatics - are evolving very quickly. The text should therefore be seen as an introduction, offering both elementary tutorials as well as more advanced applications and case studies.
Autor*in
Dirk Husmeier
Themen in »Probabilistic Modeling in Bioinformatics and Medical Informatics«
DNA STATISTICA bioinformatics biology calculus classification hidden markov model informatics medical informatics microarray microbiology modeling networks population statistics
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From the reviews:
"This book is a collection of chapters describing methods of statistical analysis of medical and biological data, with a focus on mathematical descriptions and implementing algorithms. … It will be particularly useful for those who are interested in a better understanding of artificial neutral networks … . Generally, it is a refreshing book for a statistician … giving a good description of a wide variety of complex models." (Natalia Bochkina, Significance, Vol. 3 (3), 2006)
"This book covers recent advances in the use of probabilistic models in computational molecular biology, bioinformatics and biomedicine. … A self-contained chapter on statistical inference is included as well as a discussion of Bayesian networks as a common and unifying framework for probabilistic modeling. The book has been written for researchers and students in statistics, informatics, and biological sciences … . Finally, an appendix explains the conventions and notation used throughout the book." (T. Postelnicu, Zentralblatt MATH, Vol. 1151, 2009)
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