Multivariate Biomarker Discovery
Multivariate Biomarker Discovery
$79.99
USDMultivariate biomarker discovery is increasingly important in the realm of biomedical research, and is poised to become a crucial facet of personalized medicine. This will prompt the demand for a myriad of novel biomarkers representing distinct 'omic' biosignatures, allowing selection and tailoring treatments to the various individual characteristics of a particular patient. This concise and self-contained book covers all aspects of predictive modeling for biomarker discovery based on high-dimensional data, as well as modern data science methods for identification of parsimonious and robust multivariate biomarkers for medical diagnosis, prognosis, and personalized medicine. It provides a detailed description of state-of-the-art methods for parallel multivariate feature selection and supervised learning algorithms for regression and classification, as well as methods for proper validation of multivariate biomarkers and predictive models implementing them. This is an invaluable resource for scientists and students interested in bioinformatics, data science, and related areas.
- Describes all aspects of predictive modelling for multivariate biomarker discovery, discusses common misconceptions, and provides detailed descriptions of proper methodologies
- Demonstrates how to identify multivariate biomarkers that represent real patterns in the target population, rather than spurious patterns that exist in the training data but not in the target population
- Includes two real-life multivariate biomarker discovery studies - one on multicancer early detection, which implements a method based on multistage signal enhancement and identification of essential patterns, and a second which is performed entirely in an R environment, with all scripts provided
Reviews & endorsements
'I consider this book required reading for anyone involved in biomarker discovery. It is equally relevant for newcomers to and experts in the field. It provides all the foundations explained in a succinct and easy to understand way, while being precise and detailed on the respective methods. I particularly like that the book is easy to read and factual in its assessment of the methods discussed. The book provides a perfect guide to multivariate statistics and will help the reader to avoid pitfalls.' Klaus Heumann, General Manager, LabVantage-Biomax GmbH, Germany
Product details
May 2024Adobe eBook Reader
9781009008471
0 pages
This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
- Preface
- Acknowledgments
- Part I. Framework for Multivariate Biomarker Discovery:
- 1. Introduction
- 2. Multivariate analytics based on high-dimensional data: concepts and misconceptions
- 3. Predictive modeling for biomarker discovery
- 4. Evaluation of predictive models
- 5. Multivariate feature selection
- Part II. Regression Methods for Estimation:
- 6. Basic regression methods
- 7. Regularized regression methods
- 8. Regression with random forests
- 9. Support vector regression
- Part III. Classification Methods:
- 10. Classification with random forests
- 11. Classification with support vector machines
- 12. Discriminant analysis
- 13. Neural networks and deep learning
- Part IV. Biomarker Discovery via Multistage Signal Enhancement and Identification of Essential Patterns:
- 14. Multistage signal enhancement
- 15. Essential patterns, essential variables, and interpretable biomarkers
- Part V. Multivariate Biomarker Discovery Studies:
- 16. Biomarker discovery study 1: searching for essential gene expression patterns and multivariate biomarkers that are common for multiple types of cancer
- 17. Biomarker discovery study 2: multivariate biomarkers for liver cancer
- References
- Index.
