Statistical Modeling in Biomedical Research: Contemporary Topics and Voices in the Field: Emerging Topics in Statistics and Biostatistics
Editat de Yichuan Zhao, Ding-Geng (Din) Chenen Limba Engleză Hardback – 20 mar 2020
- Next generation sequence data analysis
- Deep learning, precision medicine, and their applications
- Large scale data analysis and its applications
- Biomedical research and modeling
- Survival analysis with complex data structure and its applications.
| Toate formatele și edițiile | Preț | Express |
|---|---|---|
| Paperback (1) | 571.99 lei 6-8 săpt. | |
| Springer International Publishing – 20 mar 2021 | 571.99 lei 6-8 săpt. | |
| Hardback (1) | 638.58 lei 6-8 săpt. | |
| Springer International Publishing – 20 mar 2020 | 638.58 lei 6-8 săpt. |
Preț: 638.58 lei
Preț vechi: 751.26 lei
-15% Nou
Puncte Express: 958
Preț estimativ în valută:
122.21€ • 128.56$ • 101.97£
122.21€ • 128.56$ • 101.97£
Carte tipărită la comandă
Livrare economică 09-23 ianuarie 25
Preluare comenzi: 021 569.72.76
Specificații
ISBN-13: 9783030334154
ISBN-10: 3030334155
Pagini: 491
Ilustrații: XVIII, 491 p. 107 illus., 79 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.89 kg
Ediția:1st ed. 2020
Editura: Springer International Publishing
Colecția Springer
Seria Emerging Topics in Statistics and Biostatistics
Locul publicării:Cham, Switzerland
ISBN-10: 3030334155
Pagini: 491
Ilustrații: XVIII, 491 p. 107 illus., 79 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.89 kg
Ediția:1st ed. 2020
Editura: Springer International Publishing
Colecția Springer
Seria Emerging Topics in Statistics and Biostatistics
Locul publicării:Cham, Switzerland
Cuprins
Preface.- Part I: Next Generation Sequence Data Analysis.- 1. Modeling Species Specific Gene Expression Across Multiple Regions in the Brain.- 2. Classification of EEG Motion Artifact Signals Using Spatial ICA.- 3. Weighted K-means Clustering with Observation Weight for Single-cell Epigenomic Data.- 4. Discrete Multiple Testing in Detecting Differential Methylation Using Sequencing Data.- Part II: Deep Learning, Precision Medicine and Applications.- 5. Prediction of Functional Markers of Mass Cytometry Data via Deep Learning.- 6. Building Health Application Recommender System Using Partially Penalized Regression.- 7. Hierarchical Continuous Time Hidden Markov Model, with Application in Zero-Inflated Accelerometer Data.- Part III: Large Scale Data Analysis and its Applications.- 8. Privacy Preserving Feature Selection Via Voted Wrapper Method For Horizontally Distributed Medical Data.- 9. Improving Maize Trait through Modifying Combination of Genes.- 10. Molecular Basis of Food Classification in Traditional Chinese Medicine.- 11. Discovery Among Binary Biomarkers in Heterogeneous Populations.- Part IV: Biomedical Research and the Modelling.- 12. Heat Kernel Smoothing on Manifolds and Its Application to Hyoid Bone Growth Modeling.- 13. Optimal Projections in the Distance-Based Statistical Methods.- 14. Kernel Tests for One, Two, and K-Sample Goodness-Of-Fit: State of the Art and Implementation Considerations.- 15. Hierarchical Modeling of the Effect of Pre-exposure Prophylaxis on HIV in the US.- 16. Mathematical Model of Mouse Ventricular Myocytes Overexpressing Adenylyl Cyclase Type 5.- Part V: Survival Analysis with Complex Data Structure and its Applications.- 17. Non-Parametric Maximum Likelihood Estimator for Case-Cohort and Nested Case-Control Designs with Competing Risks Data.- Authors: Jie-Huei Wang, Chun-Hao Pan, Yi-Hau Chen and I-Shou Chang.- 18. Variable Selection in Partially Linear Proportional Hazards Model with Grouped Covariates and a Diverging Number of Parameters.- 19. Inference of Transition Probabilities in Multi-state Models using Adaptive Inverse Probability Censoring Weighting Technique.
Notă biografică
Professor Yichuan Zhao is a professor of statistics at Georgia State University. He has a joint appointment as associate member of the Neuroscience Institute, and he is also an affiliated faculty member at the School of Public Health at Georgia State University. His current research interests focus on survival analysis, empirical likelihood method, nonparametric statistics, statistical analysis of ROC curves, bioinformatics, Monte Carlo methods, high-dimensional data analysis, and statistical modeling of fuzzy systems. He has published over 90 research articles in statistics and has co-edited three books on statistics, biostatistics & data science. In addition, he has been invited to deliver more than 180 research talks nationally and internationally. Dr. Zhao has organized the Workshop Series on Biostatistics and Bioinformatics since its initiation in 2012. He also organized the 25th ICSA Applied Statistics Symposium in Atlanta as a chair of the organizing committee to great success. He is currently serving as associate editor, or on the editorial board, for several statistical journals. Dr. Zhao is an elected member of the International Statistical Institute. Professor (Din) Ding-Geng Chen is a fellow of the American Statistical Association and currently the Wallace H. Kuralt Distinguished Professor at the University of North Carolina at Chapel Hill, and an extra-ordinary professor at the University of Pretoria. He was a professor at the University of Rochester and the Karl E. Peace Endowed Eminent Scholar Chair in biostatistics at Georgia Southern University. He is also a senior consultant for biopharmaceutical and government agencies, with extensive expertise in clinical trial biostatistics and public health statistics. Professor Chen has written more than 200 refereed publications and has co-authored/co-edited 28 books on clinical trial methodology, meta-analysis, causal inference, and public health statistics.
Textul de pe ultima copertă
This edited collection discusses the emerging topics in statistical modeling for biomedical research. Leading experts in the frontiers of biostatistics and biomedical research discuss the statistical procedures, useful methods, and their novel applications in biostatistics research. Interdisciplinary in scope, the volume as a whole reflects the latest advances in statistical modeling in biomedical research, identifies impactful new directions, and seeks to drive the field forward. It also fosters the interaction of scholars in the arena, offering great opportunities to stimulate further collaborations. This book will appeal to industry data scientists and statisticians, researchers, and graduate students in biostatistics and biomedical science. It covers topics in:
- Next generation sequence data analysis
- Deep learning, precision medicine, and their applications
- Large scale data analysis and its applications
- Biomedical research and modeling
- Survival analysis with complex data structure and its applications.
Caracteristici
Includes a foundational overview of modeling in biomedical research to guide the reader in learning efficiently Covers machine learning, GWAS data analysis, sequence analysis, and survival analysis in the big data era Includes innovative statistical methods and applications in biomedical research