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Handbook of Biomarkers and Precision Medicine

The field of Biomarkers and Precision Medicine in drug development is rapidly evolving and this book presents a snapshot of exciting new approaches. By presenting a wide range of biomarker applications discussed by knowledgeable and experienced scientists readers will develop an appreciation of the scope and breadth of biomarker knowledge and find examples that will help them in their own work. Maria Freire Foundation for the National Institutes of HealthHandbook of Biomarkers and Precision Medicine provides comprehensive insights into biomarker discovery and development which has driven the new era of Precision Medicine. A wide variety of renowned experts from government academia teaching hospitals biotechnology and pharmaceutical companies share best practices examples and exciting new developments. The handbook aims to provide in-depth knowledge to research scientists students and decision makers engaged in Biomarker and Precision Medicine-centric drug development. Features:Detailed insights into biomarker discovery validation and diagnostic development with implementation strategiesLessons-learned from successful Precision Medicine case studiesA variety of exciting and emerging biomarker technologiesThe next frontiers and future challenges of biomarkers in Precision MedicineClaudio Carini Mark Fidock and Alain van Gool are internationally recognized as scientific leaders in Biomarkers and Precision Medicine. They have worked for decades in academia and pharmaceutical industry in EU USA and Asia. Currently Dr. Carini is Honorary Faculty at Kings’s College School of Medicine London UK. Dr. Fidock is Vice President of Precision Medicine Laboratories at AstraZeneca Cambridge UK. Prof. dr. van Gool is Head Translational Metabolic Laboratory at Radboud university medical school Nijmegen NL.

GBP 66.99

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Data Science and Analytics with Python

Data Science and Analytics with Python is designed for practitioners in data science and data analytics in both academic and business environments. The aim is to present the reader with the main concepts used in data science using tools developed in Python such as SciKit-learn Pandas Numpy and others. The use of Python is of particular interest given its recent popularity in the data science community. The book can be used by seasoned programmers and newcomers alike. The book is organized in a way that individual chapters are sufficiently independent from each other so that the reader is comfortable using the contents as a reference. The book discusses what data science and analytics are from the point of view of the process and results obtained. Important features of Python are also covered including a Python primer. The basic elements of machine learning pattern recognition and artificial intelligence that underpin the algorithms and implementations used in the rest of the book also appear in the first part of the book. Regression analysis using Python clustering techniques and classification algorithms are covered in the second part of the book. Hierarchical clustering decision trees and ensemble techniques are also explored along with dimensionality reduction techniques and recommendation systems. The support vector machine algorithm and the Kernel trick are discussed in the last part of the book. About the Author Dr. Jesús Rogel-Salazar is a Lead Data scientist with experience in the field working for companies such as AKQA IBM Data Science Studio Dow Jones and others. He is a visiting researcher at the Department of Physics at Imperial College London UK and a member of the School of Physics Astronomy and Mathematics at the University of Hertfordshire UK He obtained his doctorate in physics at Imperial College London for work on quantum atom optics and ultra-cold matter. He has held a position as senior lecturer in mathematics as well as a consultant in the financial industry since 2006. He is the author of the book Essential Matlab and Octave also published by CRC Press. His interests include mathematical modelling data science and optimization in a wide range of applications including optics quantum mechanics data journalism and finance.

GBP 52.99

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A Concise Introduction to Statistical Inference

This short book introduces the main ideas of statistical inference in a way that is both user friendly and mathematically sound. Particular emphasis is placed on the common foundation of many models used in practice. In addition the book focuses on the formulation of appropriate statistical models to study problems in business economics and the social sciences as well as on how to interpret the results from statistical analyses. The book will be useful to students who are interested in rigorous applications of statistics to problems in business economics and the social sciences as well as students who have studied statistics in the past but need a more solid grounding in statistical techniques to further their careers. Jacco Thijssen is professor of finance at the University of York UK. He holds a PhD in mathematical economics from Tilburg University Netherlands. His main research interests are in applications of optimal stopping theory stochastic calculus and game theory to problems in economics and finance. Professor Thijssen has earned several awards for his statistics teaching. | A Concise Introduction to Statistical Inference

GBP 180.00

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Economic Evaluation of Cancer Drugs Using Clinical Trial and Real-World Data

Cancer is a major healthcare burden across the world and impacts not only the people diagnosed with various cancers but also their families carers and healthcare systems. With advances in the diagnosis and treatment more people are diagnosed early and receive treatments for a disease where few treatments options were previously available. As a result the survival of patients with cancer has steadily improved and in most cases patients who are not cured may receive multiple lines of treatment often with financial consequences for the patients insurers and healthcare systems. Although many books exist that address economic evaluation Economic Evaluation of Cancer Drugs using Clinical Trial and Real World Data is the first unified text that specifically addresses the economic evaluation of cancer drugs. The authors discuss how to perform cost-effectiveness analyses while emphasising the strategic importance of designing cost-effectiveness into cancer trials and building robust economic evaluation models that have a higher chance of reimbursement if truly cost-effective. They cover the use of real-world data using cancer registries and discuss how such data can support or complement clinical trials with limited follow up. Lessons learned from failed reimbursement attempts factors predictive of successful reimbursement and the different payer requirements across major countries including US Australia Canada UK Germany France and Italy are also discussed. The book includes many detailed practical examples case studies and thought-provoking exercises for use in classroom and seminar discussions. Iftekhar Khan is a medical statistician and health economist and a lead statistician at Oxford Unviersity’s Center for Statistics in Medicine. Professor Khan is also a Senior Research Fellow in Health Economics at University of Warwick and is a Senior Statistical Assessor within the Licensing Division of the UK Medicine and Health Regulation Agency. Ralph Crott is a former professor in Pharmacoeconomics at the University of Montreal in Quebec Canada and former head of the EORTC Health Economics Unit and former senior health economist at the Belgian HTA organization. Zahid Bashir has over twelve years experience working in the pharmaceutical industry in medical affairs and oncology drug development where he is involved in the design and execution of oncology clinical trials and development of reimbursement dossiers for HTA submission. | Economic Evaluation of Cancer Drugs Using Clinical Trial and Real-World Data

GBP 44.99

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Solution Techniques for Elementary Partial Differential Equations

In my opinion this is quite simply the best book of its kind that I have seen thus far. —Professor Peter Schiavone University of Alberta from the Foreword to the Fourth Edition Praise for the previous editions An ideal tool for students taking a first course in PDEs as well as for the lecturers who teach such courses. —Marian Aron Plymouth University UK This is one of the best books on elementary PDEs this reviewer has read so far. Highly recommended. —CHOICE Solution Techniques for Elementary Partial Differential Equations Fourth Edition remains a top choice for a standard undergraduate-level course on partial differential equations (PDEs). It provides a streamlined direct approach to developing students’ competence in solving PDEs and offers concise easily understood explanations and worked examples that enable students to see the techniques in action. New to the Fourth Edition Two additional sections A larger number and variety of worked examples and exercises A companion pdf file containing more detailed worked examples to supplement those in the book which can be used in the classroom and as an aid to online teaching

GBP 56.99

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A First Course in Machine Learning

A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings and goes all the way to the frontiers of the subject such as infinite mixture models GPs and MCMC. —Devdatt Dubhashi Professor Department of Computer Science and Engineering Chalmers University Sweden This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade. —Daniel Barbara George Mason University Fairfax Virginia USA The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling inference and prediction providing ‘just in time’ the essential background on linear algebra calculus and probability theory that the reader needs to understand these concepts. —Daniel Ortiz-Arroyo Associate Professor Aalborg University Esbjerg Denmark I was impressed by how closely the material aligns with the needs of an introductory course on machine learning which is its greatest strength…Overall this is a pragmatic and helpful book which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months. —David Clifton University of Oxford UK The first edition of this book was already an excellent introductory text on machine learning for an advanced undergraduate or taught masters level course or indeed for anybody who wants to learn about an interesting and important field of computer science. The additional chapters of advanced material on Gaussian process MCMC and mixture modeling provide an ideal basis for practical projects without disturbing the very clear and readable exposition of the basics contained in the first part of the book. —Gavin Cawley Senior Lecturer School of Computing Sciences University of East Anglia UK This book could be used for junior/senior undergraduate students or first-year graduate students as well as individuals who want to explore the field of machine learning…The book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective. —Guangzhi Qu Oakland University Rochester Michigan USA

GBP 39.99

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