Name: Analyzing Network Data in Biology and Medicine: An Interdisciplinary Textbook for Biological, Medical and Computational Scientists (en Inglés)
SKU: 51902060
Price: 1634.86 MXN
Availability: InStock
Author: Przulj, Natasa
ISBN: 9781108432238

portada Analyzing Network Data in Biology and Medicine: An Interdisciplinary Textbook for Biological, Medical and Computational Scientists (en Inglés)

Analyzing Network Data in Biology and Medicine: An Interdisciplinary Textbook for Biological, Medical and Computational Scientists (en Inglés)

Formato

Libro Físico

Ilustrado por

Natasa Przulj

Editorial

Cambridge University Press

Autor

Przulj, Natasa

Año

2019

Idioma

Inglés

N° páginas

643

Encuadernación

Tapa Blanda

Dimensiones

24.9x21.3x2.5 cm

Peso

1.27 kg.

ISBN13

9781108432238

N° edición

Categorías

Medicina general y de familia
Genética (no médica)

Analyzing Network Data in Biology and Medicine: An Interdisciplinary Textbook for Biological, Medical and Computational Scientists (en Inglés)

Natasa Przulj (Ilustrado por) · Cambridge University Press · Tapa Blanda

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Reseña del libro "Analyzing Network Data in Biology and Medicine: An Interdisciplinary Textbook for Biological, Medical and Computational Scientists (en Inglés)"

The increased and widespread availability of large network data resources in recent years has resulted in a growing need for effective methods for their analysis. The challenge is to detect patterns that provide a better understanding of the data. However, this is not a straightforward task because of the size of the data sets and the computer power required for the analysis. The solution is to devise methods for approximately answering the questions posed, and these methods will vary depending on the data sets under scrutiny. This cutting-edge text introduces biological concepts and biotechnologies producing the data, graph and network theory, cluster analysis and machine learning, before discussing the thought processes and creativity involved in the analysis of large-scale biological and medical data sets, using a wide range of real-life examples. Bringing together leading experts, this text provides an ideal introduction to and insight into the interdisciplinary field of network data analysis in biomedicine.