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Bioinformatics and Biomarker Discovery: "Omic" Data Analysis for Personalized Medicine
John Wiley and Sons Ltd, Feb 2010, Pages: 248
This book is designed to introduce biologists, clinicians and computational researchers to fundamental data analysis principles, techniques and tools for supporting the discovery of biomarkers and the implementation of diagnostic/prognostic systems.
The focus of the book is on how fundamental statistical and data mining approaches can support biomarker discovery and evaluation, emphasising applications based on different types of 'omic' data. The book also discusses design factors, requirements and techniques for disease screening, diagnostic and prognostic applications.
Readers are provided with the knowledge needed to assess the requirements, computational approaches and outputs in disease biomarker research. Commentaries from guest experts are also included, containing detailed discussions of methodologies and applications based on specific types of 'omic' data, as well as their integration. Covers the main range of data sources currently used for biomarker discovery
- Covers the main range of data sources currently used for biomarker discovery
- Puts emphasis on concepts, design principles and methodologies that can be extended or tailored to more specific applications
- Offers principles and methods for assessing the bioinformatic/biostatistic limitations, strengths and challenges in biomarker discovery studies
- Discusses systems biology approaches and applications
- Includes expert chapter commentaries to further discuss relevance of techniques, summarize biological/clinical implications and provide alternative interpretations
A research-level book, which will introduce biologists and cardiologists to fundamental data analysis principles, techniques and tools for supporting the discovery of biomarkers and diagnostic/prognostic prediction systems. The book can, however, also be used by computational biologists and bioinformaticians doing research in cardiovascular research.
The focus will be on how fundamental statistical and data mining approaches can be used to support biomarker discovery and evaluation. It will emphasize applications using cardiovascular omic data and discuss design factors, requirements and techniques for disease screening, diagnostic and prognostic applications.
The book will consist of foundation chapters, which will provide readers with the knowledge needed to assess the requirements, design factors and outputs of disease biomarker research. These will be followed by detailed discussions of methodologies and applications based on specific types of omic data, as well as their integration. Such chapters will reflect the how and what aspects of these research areas. Concluding chapters will focus on the critical assessment of current knowledge gaps, bioinformatic challenges and emerging research directions. This will underscore the why and when aspects of problems and applications.
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