Multivariate Biomarker Discovery
portes grátis
Multivariate Biomarker Discovery
Data Science Methods for Efficient Analysis of High-Dimensional Biomedical Data
Dziuda, Darius M.
Cambridge University Press
05/2024
300
Dura
Inglês
9781316518700
Pré-lançamento - envio 15 a 20 dias após a sua edição
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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.
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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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
