Multivariate Biomarker Discovery
Data Science Methods for Efficient Analysis of High-Dimensional Biomedical Data

A concise guide to all aspects of predictive modeling for biomarker discovery for medical diagnosis, prognosis, and personalized medicine.

Darius M. Dziuda (Author)

9781316518700, Cambridge University Press

Hardback, published 6 June 2024

294 pages
25 x 17.7 x 2.2 cm, 0.67 kg

'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

Multivariate 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.

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.

Subject Areas: Genetics [non-medical PSAK]