Statistical Modeling and Machine Learning for Molecular Biology

Alan Moses

ISBN 10: 1482258595 / ISBN 13: 9781482258592

Editore: Chapman And Hall/CRC, 2017

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nach der Bestellung gedruckt Neuware - Printed after ordering - The book covers several of the major data analysis techniques used to analyze data from high-throughput molecular biology and genomics experiments. It also explains the major concepts behind most of the popular techniques and examines some of the simpler techniques in detail.

Codice articolo 9781482258592

Dati bibliografici

• Titolo: Statistical Modeling and Machine Learning for Molecular Biology
• Editore: Chapman And Hall/CRC
• Data di pubblicazione: 2017
• Lingua: Inglese
• ISBN 10: 1482258595
• ISBN 13: 9781482258592
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 234 L x 156 A x 15 P (millimetri) 234 millimetri di larghezza per 156 millimetri di altezza per 15 millimetri di profondità
• Peso: 433 grammi

Descrizione articolo

Riassunto

Molecular biologists are performing increasingly large and complicated experiments, but often have little background in data analysis. The book is devoted to teaching the statistical and computational techniques molecular biologists need to analyze their data. It explains the big-picture concepts in data analysis using a wide variety of real-world molecular biological examples such as eQTLs, ortholog identification, motif finding, inference of population structure, protein fold prediction and many more. The book takes a pragmatic approach, focusing on techniques that are based on elegant mathematics yet are the simplest to explain to scientists with little background in computers and statistics.

Informazioni sull?autore

Alan M Moses is currently Associate Professor and Canada Research Chair in Computational Biology in the Departments of Cell & Systems Biology and Computer Science at the University of Toronto. His research touches on many of the major areas in computational biology, including DNA and protein sequence analysis, phylogenetic models, population genetics, expression profiles, regulatory network simulations and image analysis.