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 | : Kenneth P. Burnham |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 512 |
| Release | : 2007-05-28 |
| Genre | : Mathematics |
| ISBN | : 0387224564 |
A unique and comprehensive text on the philosophy of model-based data analysis and strategy for the analysis of empirical data. The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. It contains several new approaches to estimating model selection uncertainty and incorporating selection uncertainty into estimates of precision. An array of examples is given to illustrate various technical issues. The text has been written for biologists and statisticians using models for making inferences from empirical data.
| Author | : Stephan Schmidt |
| Publisher | : Springer |
| Total Pages | : 570 |
| Release | : 2014-12-01 |
| Genre | : Medical |
| ISBN | : 1493913042 |
This comprehensive volume provides an update on the current state of pharmacometrics in drug development. It consists of nineteen chapters all written by leading scientists from the pharmaceutical industry, regulatory agencies and academia. After an introduction of the basic pharmacokinetic and pharmacodynamic concepts of pharmacometrics in drug development, the book presents numerous examples of specific applications that utilize pharmacometrics with modeling and simulations over a variety of therapeutic areas, including pediatrics, diabetes, obesity, infections, psychiatrics, Alzheimer’s disease, and dermatology, among others. The examples illustrate how results from all phases of drug development can be integrated in a more timely and cost-effective process. Applying pharmacometric decision tools during drug development can allow objective, data-based decision making. At the same time, the process can identify redundant or unnecessary experiments as well as some costly clinical trials that can be avoided. In addition to cost saving by expedited development of successful drug candidates, pharmacometrics has an important economic impact in drug product selection. Unsuccessful drug candidates can be identified early and discontinued without expending efforts required for additional studies and allocating limited resources. Hence, pharmacometric modeling and simulation has become a powerful tool to bring new and better medications to the patient at a faster pace and with greater probability of success.
| Author | : Ahmad Lotfi |
| Publisher | : Springer |
| Total Pages | : 399 |
| Release | : 2018-08-10 |
| Genre | : Technology & Engineering |
| ISBN | : 3319979825 |
This book presents the latest trends in and approaches to computational intelligence research and its application to intelligent systems. It covers a long list of interconnected research areas, such as fuzzy systems, neural networks, evolutionary computation, clustering and classification, machine learning, data mining, cognition and robotics, and deep learning. The individual chapters are based on peer-reviewed contributions presented at the 18th Annual UK Workshop on Computational Intelligence (UKCI-2018), held in Nottingham, UK on September 5-7, 2018. The book puts a special emphasis on novel methods and reports on their use in a wide range of applications areas, thus providing both academics and professionals with a comprehensive and timely overview of new trends in computational intelligence.
| 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.
| 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 | : Joel S. Owen |
| Publisher | : John Wiley & Sons |
| Total Pages | : 0 |
| Release | : 2014-07-08 |
| Genre | : Medical |
| ISBN | : 9780470582299 |
This book provides a user-friendly, hands-on introduction to the Nonlinear Mixed Effects Modeling (NONMEM) system, the most powerful tool for pharmacokinetic / pharmacodynamic analysis. • Introduces requisite background to using Nonlinear Mixed Effects Modeling (NONMEM), covering data requirements, model building and evaluation, and quality control aspects • Provides examples of nonlinear modeling concepts and estimation basics with discussion on the model building process and applications of empirical Bayesian estimates in the drug development environment • Includes detailed chapters on data set structure, developing control streams for modeling and simulation, model applications, interpretation of NONMEM output and results, and quality control • Has datasets, programming code, and practice exercises with solutions, available on a supplementary website
| Author | : Luca Marchetti |
| Publisher | : Springer |
| Total Pages | : 245 |
| Release | : 2017-09-27 |
| Genre | : Computers |
| ISBN | : 3319631136 |
This book explains the state-of-the-art algorithms used to simulate biological dynamics. Each technique is theoretically introduced and applied to a set of modeling cases. Starting from basic simulation algorithms, the book also introduces more advanced techniques that support delays, diffusion in space, or that are based on hybrid simulation strategies. This is a valuable self-contained resource for graduate students and practitioners in computer science, biology and bioinformatics. An appendix covers the mathematical background, and the authors include further reading sections in each chapter.
| Author | : Marie Davidian |
| Publisher | : Routledge |
| Total Pages | : 360 |
| Release | : 2017-11-01 |
| Genre | : Mathematics |
| ISBN | : 1351428152 |
Nonlinear measurement data arise in a wide variety of biological and biomedical applications, such as longitudinal clinical trials, studies of drug kinetics and growth, and the analysis of assay and laboratory data. Nonlinear Models for Repeated Measurement Data provides the first unified development of methods and models for data of this type, with a detailed treatment of inference for the nonlinear mixed effects and its extensions. A particular strength of the book is the inclusion of several detailed case studies from the areas of population pharmacokinetics and pharmacodynamics, immunoassay and bioassay development and the analysis of growth curves.
| Author | : Tom M. Mitchell |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 413 |
| Release | : 2012-12-06 |
| Genre | : Computers |
| ISBN | : 1461322790 |
One of the currently most active research areas within Artificial Intelligence is the field of Machine Learning. which involves the study and development of computational models of learning processes. A major goal of research in this field is to build computers capable of improving their performance with practice and of acquiring knowledge on their own. The intent of this book is to provide a snapshot of this field through a broad. representative set of easily assimilated short papers. As such. this book is intended to complement the two volumes of Machine Learning: An Artificial Intelligence Approach (Morgan-Kaufman Publishers). which provide a smaller number of in-depth research papers. Each of the 77 papers in the present book summarizes a current research effort. and provides references to longer expositions appearing elsewhere. These papers cover a broad range of topics. including research on analogy. conceptual clustering. explanation-based generalization. incremental learning. inductive inference. learning apprentice systems. machine discovery. theoretical models of learning. and applications of machine learning methods. A subject index IS provided to assist in locating research related to specific topics. The majority of these papers were collected from the participants at the Third International Machine Learning Workshop. held June 24-26. 1985 at Skytop Lodge. Skytop. Pennsylvania. While the list of research projects covered is not exhaustive. we believe that it provides a representative sampling of the best ongoing work in the field. and a unique perspective on where the field is and where it is headed.
