Applied Computational Genomics
2nd Edition
Published on 22. September 2018
Book
Hardback
VII, 150 pages
978-981-13-1070-6 (ISBN)
Description
The volume provides a review of statistical development and application in the area of human genomics, including candidate gene mapping, linkage analysis, population-based genome-wide association, exon sequencing, and whole genome sequencing analysis. The authors are extremely experienced in the field of statistical genomics and will give a detailed introduction to the evolution of the field, as well as critical comments on the advantages and disadvantages of the proposed statistical models. The future directions of translational biology will also be described.
More details
Series
Edition
Second Edition 2018
Language
English
Place of publication
Singapore
Singapore
Target group
Professional and scholarly
Edition type
Revised edition
Illustrations
1 s/w Abbildung, 13 farbige Abbildungen
VII, 150 p. 14 illus., 13 illus. in color.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 14 mm
Weight
442 gr
ISBN-13
978-981-13-1070-6 (9789811310706)
DOI
10.1007/978-981-13-1071-3
Schweitzer Classification
Other editions
Additional editions
Yin Yao
Applied Computational Genomics
Book
02/2019
2nd Edition
Springer
€106.99
Shipment within 15-20 days
Yin Yao
Applied Computational Genomics
E-Book
09/2018
2nd Edition
Springer
€96.29
Available for download
Person
Dr. Yin Yao is investigator in the Intramural Research Program at the National Institute of Mental Health, Bethesda, Maryland USA.
Content
Introduction.- Exploring Polygenic Overlap Between ADHD and OCD.- Concepts of Genetic Epidemiology.- Rare Variants Analysis in Unrelated Individuals.- Whole Genome Association of Treatment Response in OCD.- QTL Mapping of Molecular Traits for Studies of Human Complex Diseases.- From Family Study to Population Study: A History of Genetic Mapping for Nasopharyngeal Carcinoma (NPC).- Test for Nonlinear Dependence of Two Continuous Variables.- Analytical Approaches for Exome Sequence Data.- Machine Learning Approaches: Data Integration for Disease Prediction and Prognosis.- OCD Genomics and Future Looks.