An Information Theoretic Approach To High Dimensional Pharmacometrics
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Author | : Jonathan Knights |
Publisher | : |
Total Pages | : 304 |
Release | : 2013 |
Genre | : |
ISBN | : |
The following dissertation develops an information-theoretic computational framework, and shows applications for analysis of high-dimensional datasets such as those routinely encountered when conducting pharmacogenetic/genomic clinical trials. The successful application of information-theoretic concepts in pharmaceutical datasets provides a novel set of pharmacometric tools that may be leveraged to increase learning on genome scale datasets. A series of novel algorithms coded in the javaTM programming language with computational roots in information theory were extended and utilized as the basis for the methodology. Simulations and actual clinical datasets representing a broad range of complexity were utilized to highlight the capabilities of the computational approach on high-dimensional datasets. Additionally, the work was compared to existing methodologies on both theoretical and practical levels.^The results suggest that an information-theoretic analytical platform offers an appropriately flexible and computationally efficient basis for performing interaction analyses on high-dimensional data sets. In most cases, the proposed methods performed comparably to, or better than, existing methodologies when the size of the data set was not prohibitive for traditional approaches: These results held across all levels of complexity, and for clinical data as well simulated data. In situations where multiple types of relationships may exist and there is no specific need for a structural parameterization, the information-theoretic approach proposed here may serve as an appropriate analytical platform, capable of detecting novel interactions and informative relationships.^The algorithms that have been extended here are computationally efficient enough to allow detection of higher-order relationships in genome-scale data sets on common laboratory computers, negating the need for access to sophisticated computational facilities. Thus, the advancements realized by this work are as much theoretical as they are practical.
Author | : Ene I. Ette |
Publisher | : John Wiley & Sons |
Total Pages | : 1236 |
Release | : 2013-03-14 |
Genre | : Medical |
ISBN | : 1118679512 |
Pharmacometrics is the science of interpreting and describing pharmacology in a quantitative fashion. The pharmaceutical industry is integrating pharmacometrics into its drug development program, but there is a lack of and need for experienced pharmacometricians since fewer and fewer academic programs exist to train them. Pharmacometrics: The Science of Quantitative Pharmacology lays out the science of pharmacometrics and its application to drug development, evaluation, and patient pharmacotherapy, providing a comprehensive set of tools for the training and development of pharmacometricians. Edited and written by key leaders in the field, this flagship text on pharmacometrics: Integrates theory and practice to let the reader apply principles and concepts. Provides a comprehensive set of tools for training and developing expertise in the pharmacometric field. Is unique in including computer code information with the examples. This volume is an invaluable resource for all pharmacometricians, statisticians, teachers, graduate and undergraduate students in academia, industry, and regulatory agencies.
Author | : Zinnia P. Parra-Guillen |
Publisher | : Frontiers Media SA |
Total Pages | : 117 |
Release | : 2024-02-21 |
Genre | : Science |
ISBN | : 2832545033 |
Pharmacometrics represents a strategy to optimize and rationalize decision-making process integrating information on drug behavior, pharmacological response, and disease progression both in the drug development phases and in their clinical use. Pharmacometrics focuses on characterizing the pharmacokinetic and pharmacodynamic behavior of one or several active ingredients through the development of mathematical and statistical models that allow characterizing both the average behavior in the population and the different sources of variability. Currently, pharmacometrics has transformed drug development and therapeutic use paradigm, which yield to the recognition by the main regulatory agencies (FDA, EMA, and PMDA).
Author | : DavidW.A. Bourne |
Publisher | : Routledge |
Total Pages | : 154 |
Release | : 2018-05-02 |
Genre | : Medical |
ISBN | : 1351433245 |
A concise guide to mathematical modeling and analysis of pharmacokinetic data, this book contains valuable methods for maximizing the information obtained from given data. It is an ideal resource for scientists, scholars, and advanced students.
Author | : Carl Edward Rasmussen |
Publisher | : MIT Press |
Total Pages | : 266 |
Release | : 2005-11-23 |
Genre | : Computers |
ISBN | : 026218253X |
A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.
Author | : Peter L. Bonate |
Publisher | : Springer Science & Business Media |
Total Pages | : 634 |
Release | : 2011-07-01 |
Genre | : Medical |
ISBN | : 1441994858 |
This is a second edition to the original published by Springer in 2006. The comprehensive volume takes a textbook approach systematically developing the field by starting from linear models and then moving up to generalized linear and non-linear mixed effects models. Since the first edition was published the field has grown considerably in terms of maturity and technicality. The second edition of the book therefore considerably expands with the addition of three new chapters relating to Bayesian models, Generalized linear and nonlinear mixed effects models, and Principles of simulation. In addition, many of the other chapters have been expanded and updated.
Author | : |
Publisher | : |
Total Pages | : 2408 |
Release | : 1965 |
Genre | : American literature |
ISBN | : |
Author | : Jan Beirlant |
Publisher | : John Wiley & Sons |
Total Pages | : 522 |
Release | : 2006-03-17 |
Genre | : Mathematics |
ISBN | : 0470012374 |
Research in the statistical analysis of extreme values has flourished over the past decade: new probability models, inference and data analysis techniques have been introduced; and new application areas have been explored. Statistics of Extremes comprehensively covers a wide range of models and application areas, including risk and insurance: a major area of interest and relevance to extreme value theory. Case studies are introduced providing a good balance of theory and application of each model discussed, incorporating many illustrated examples and plots of data. The last part of the book covers some interesting advanced topics, including time series, regression, multivariate and Bayesian modelling of extremes, the use of which has huge potential.
Author | : |
Publisher | : |
Total Pages | : 1716 |
Release | : 2001-04 |
Genre | : Medicine |
ISBN | : |
Vols. for 1963- include as pt. 2 of the Jan. issue: Medical subject headings.
Author | : Giuseppe Bonaccorso |
Publisher | : Packt Publishing Ltd |
Total Pages | : 352 |
Release | : 2017-07-24 |
Genre | : Computers |
ISBN | : 1785884514 |
Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This Book Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide. Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation. Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide. Who This Book Is For This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here. What You Will Learn Acquaint yourself with important elements of Machine Learning Understand the feature selection and feature engineering process Assess performance and error trade-offs for Linear Regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector machines Implement clusters to a dataset Explore the concept of Natural Processing Language and Recommendation Systems Create a ML architecture from scratch. In Detail As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously. On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem. Style and approach An easy-to-follow, step-by-step guide that will help you get to grips with real -world applications of Algorithms for Machine Learning.