Isbn: 9788743812463 - biomedical data science: a step-by-step guide to analysis and interpretation (river publishers rapids series in computational approaches in biology and medicine) (5 resultados)

Condición: New. Simone G. Riva is a senior computational and machine learning scientist in genomics at the University of Oxford. As a computational biologist working at the intersection of computer science and genomics, he specialises in developing advanced machi.

Buch. Condición: Neu. Neuware - Biomedical Data Science: A Step-by-Step Guide to Analysis and Interpretation is a practical roadmap for transforming complex biomedical data into reliable insights. Designed for readers at the crossroads of biology, medicine, and computation, the book walks readers through the entire lifecycle of analysis, from formulating clear questions to designing robust studies, quantifying uncertainty, building and validating models, and interpreting results responsibly.Instead of overwhelming the reader with derivations, it emphasises conceptual clarity, reproducibility, and interpretability, linking key ideas with Python workflows using widely adopted libraries. Core statistical tools (estimation, confidence intervals, hypothesis testing, multiple testing) are integrated with essential machine-learning practices (cross-validation, metrics, baseline vs. null models, sanity checks, and model explanation).An end-to-end clinical case study ties everything together-demonstrating how design decisions, preprocessing, statistical analysis, and predictive modelling collectively influence conclusions and clinical significance. As the first volume of the River Series, this book establishes a practical, open, and interdisciplinary approach to data-driven biomedicine, guiding readers to 'get it right' from the outset.…

Hardcover. Condición: new. Hardcover. Biomedical Data Science: A Step-by-Step Guide to Analysis and Interpretation is a practical roadmap for transforming complex biomedical data into reliable insights. Designed for readers at the crossroads of biology, medicine, and computation, the book walks readers through the entire lifecycle of analysis, from formulating clear questions to designing robust studies, quantifying uncertainty, building and validating models, and interpreting results responsibly.Instead of overwhelming the reader with derivations, it emphasises conceptual clarity, reproducibility, and interpretability, linking key ideas with Python workflows using widely adopted libraries. Core statistical tools (estimation, confidence intervals, hypothesis testing, multiple testing) are integrated with essential machine-learning practices (cross-validation, metrics, baseline vs. null models, sanity checks, and model explanation).An end-to-end clinical case study ties everything togetherdemonstrating how design decisions, preprocessing, statistical analysis, and predictive modelling collectively influence conclusions and clinical significance. As the first volume of the River Series, this book establishes a practical, open, and interdisciplinary approach to data-driven biomedicine, guiding readers to get it right from the outset. Designed for readers at the crossroads of biology, medicine, and computation, this book walks readers through the entire lifecycle of analysis, from formulating clear questions to designing robust studies, quantifying uncertainty, building and validating models, and interpreting results responsibly. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Hardcover. Condición: new. Hardcover. Biomedical Data Science: A Step-by-Step Guide to Analysis and Interpretation is a practical roadmap for transforming complex biomedical data into reliable insights. Designed for readers at the crossroads of biology, medicine, and computation, the book walks readers through the entire lifecycle of analysis, from formulating clear questions to designing robust studies, quantifying uncertainty, building and validating models, and interpreting results responsibly.Instead of overwhelming the reader with derivations, it emphasises conceptual clarity, reproducibility, and interpretability, linking key ideas with Python workflows using widely adopted libraries. Core statistical tools (estimation, confidence intervals, hypothesis testing, multiple testing) are integrated with essential machine-learning practices (cross-validation, metrics, baseline vs. null models, sanity checks, and model explanation).An end-to-end clinical case study ties everything togetherdemonstrating how design decisions, preprocessing, statistical analysis, and predictive modelling collectively influence conclusions and clinical significance. As the first volume of the River Series, this book establishes a practical, open, and interdisciplinary approach to data-driven biomedicine, guiding readers to get it right from the outset. Designed for readers at the crossroads of biology, medicine, and computation, this book walks readers through the entire lifecycle of analysis, from formulating clear questions to designing robust studies, quantifying uncertainty, building and validating models, and interpreting results responsibly. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Hardcover. Condición: new. Hardcover. Biomedical Data Science: A Step-by-Step Guide to Analysis and Interpretation is a practical roadmap for transforming complex biomedical data into reliable insights. Designed for readers at the crossroads of biology, medicine, and computation, the book walks readers through the entire lifecycle of analysis, from formulating clear questions to designing robust studies, quantifying uncertainty, building and validating models, and interpreting results responsibly.Instead of overwhelming the reader with derivations, it emphasises conceptual clarity, reproducibility, and interpretability, linking key ideas with Python workflows using widely adopted libraries. Core statistical tools (estimation, confidence intervals, hypothesis testing, multiple testing) are integrated with essential machine-learning practices (cross-validation, metrics, baseline vs. null models, sanity checks, and model explanation).An end-to-end clinical case study ties everything togetherdemonstrating how design decisions, preprocessing, statistical analysis, and predictive modelling collectively influence conclusions and clinical significance. As the first volume of the River Series, this book establishes a practical, open, and interdisciplinary approach to data-driven biomedicine, guiding readers to get it right from the outset. Designed for readers at the crossroads of biology, medicine, and computation, this book walks readers through the entire lifecycle of analysis, from formulating clear questions to designing robust studies, quantifying uncertainty, building and validating models, and interpreting results responsibly. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…