Statistics and Informatics in Molecular Cancer Research

Erscheinungsjahr 2009
ISBN: 978-0-19-953287-2
Verlag: Oxford University Press

Buch, Englisch, 218 Seiten, Print PDF, Format (B × H): 159 mm x 240 mm, Gewicht: 442 g

Molecular understanding of cancer and cancer progression is at the forefront of many research programs today. High-throughput array technologies and other modern molecular techniques produce a wealth of molecular data about the structure, and function of cells, tissues, and organisms. Correctly analyzed and interpreted these data hold the promise of bringing new markers for prognostic and diagnostic use, for new treatment schemes, and of gaining new biological insight into the evolution of cancer and its molecular, pathological, and clinical consequences.

Aimed at graduates and researchers, this book discusses novel advances in informatics and statistics in molecular cancer research. Through eight chapters from carefully chosen experts it brings the reader up to date with specific topics in cancer research, how the topics give rise to development of new informatics and statistics tools, and how the tools can be applied. The focus of the book is to give the reader an understanding of key concepts and tools, rather than focusing on technical issues.

A main theme is the extensive use of array technologies in modern cancer research - gene expression and exon arrays, SNP and copy number arrays, and methylation arrays - to derive quantitative and qualitative statements about cancer, its progression and aetiology, and to understand how these technologies on one hand allow us learn about cancer tissue as a complex system and on the other hand allow us to pinpoint key genes and events as crucial for the development of the disease.

Autoren/Hrsg.

Wiuf, Carsten

Andersen, Claus L.

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Klinische und Innere Medizin Onkologie, Krebsforschung
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung

Inhaltsverzeichnis

- Preface

- 1: Emily Webb and Richard Houlston: Association Studies

- 2: Cameron Brennan: Methods for DNA Copy Number Derivations

- 3: Carsten Wiuf, Philippe Lamy, and Claus L Andersen: Methods for Derivation of LOH and Allelic Copy Numbers Using SNP Arrays

- 4: Outi Monni and Sampsa Hautaniemi: Bioinformatics of gene expression and copy number data integration

- 5: Fabian Model, Jörn Lewin, Catherine Lofton-Day, and Gunter Weiss: Analysis of DNA Methylation in Cancer

- 6: Ming Yi and Robert M Stephens: Pathway Analysis: Pathway Signatures and Classification

- 7: Wendy Lockwood Banka, Matthew J Anstett, and Daniel R Rhodes: Two Methods for Comparing Genomic Data Across Independent Studies in Cancer Research: Meta-analysis and Oncomine Concepts Map

- 8: Lue Ping Zhao and Jessica Andriesen: Bioinformatic Approaches to the Analysis of Alternative Splicing Variants in Cancer Biology

- Index

Über Autor(innen)

Carsten Wiuf obtained his PhD in mathematical biology from the University of Aarhus in 1998. Afterwards he spent 4 years in Oxford at the Department of Statistics before joining a biotech company in Boston. In 2003 he became Professor of Bioinformatics at the University of Aarhus. He has co-authored the book Gene Genealogies, Variation and Evolution (OUP).

Claus L. Andersen earned his PhD in cancer biology from the University of Aarhus in 2002. In 2002 he became an assistant professor at the University of Aarhus and later in 2005 an associated professor. Today he is heading the colorectal cancer research group at the Molecular Diagnostic Laboratory, Aarhus University Hospital.

Both have worked on informatics approaches to the analysis of molecular cancer data and have practical as well as theoretical experience with development of bioinformatics and statistical methods for analysis of molecular data.