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Biomarkers in drug discovery and development : A handbook of practice, application, and strategy
Discusses biomarker characterization and validation and applications throughout drug discovery and development. Explains where proper use of biomarkers can substantively impact drug development timelines and costs, enable selection of better compounds and reduce late stage attrition, and facilitate personalized medicine. Helps readers get a better understanding of biomarkers and how to use them, for example which are accepted by regulators and which still non-validated and exploratory. Updates developments in genomic sequencing, and application of large data sets into pre-clinical and clinical testing; and adds new material on data mining, economics, and decision making, personal genetic tools, and wearable monitoring. Includes case studies of biomarkers that have helped and hindered decision making
Biological and medical data analysis ; Vol. 4345 : 7th International Symposium, ISBMDA 2006, Thessaloniki, Greece, December 7-8, 2006. Proceeding
This book constitutes the refereed proceedings of the 7th International Symposium on Biological and Medical Data Analysis, ISBMDA 2006, held in Thessaloniki, Greece, December 2006. Coverage in this volume includes functional genomics, sequence analysis, biomedical models, information modeling, biomedical signal processing, biomedical image analysis, biomedical data analysis, as well as decision support systems and diagnostic tools.
Biological and medical data analysis ; Vol. 3745 ; 6th International symposium, ISBMDA 2005, Aveiro, Portugal, November 10-11, 2005, Proceedings
The 6th International Symposium on Biological and Medical Data Analysisaimed to become a place where researchersinvolved in these diverse but increas-ingly complementary areas could meet topresent and discuss their scientificresults.The papers in this volume discuss issues from statistical models to archi-tectures and applications to bioinformatics and biomedicine. They cover bothpractical experience and novel research ideas and concepts.
Bio-inspired credit risk analysis : Computational intelligence with support vector machines
Credit risk analysis is one of the most important topics in the field of financial risk management. Due to recent financial crises and regulatory concern of Basel II, credit risk analysis has been the major focus of financial and banking industry. Especially for some credit-granting institutions such as commercial banks and credit companies, the ability to discriminate good customers from bad ones is crucial. The need for reliable quantitative models that predict defaults accurately is imperative so that the interested parties can take either preventive or corrective action. Hence credit risk analysis becomes very important for sustainability and profit of enterprises. In such backgrounds, this book tries to integrate recent emerging support vector machines and other computational intelligence techniques that replicate the principles of bio-inspired information processing to create some innovative methodologies for credit risk analysis and to provide decision support information for interested parties.
Bioethics in Law
The idea for Bioethics in Law began more than a decade ago, while I was studying social science and law. I was parti- larly interested in the collaborations that comprised social s- ence in law. Economic and social data in the pioneering Brandeis brief had been used to defend an early 20th-century labor law; surveys of consumer confusion had helped resolve trademark - fringement cases; psychologists’ predictions of future violence had informed capital sentencing decisions. Additionally, Kenneth Clark’s “doll studies,” cited by the Supreme Court in Brown v. Board of Education, had helped change the course of American 1 history.
Big Data Science in Finance
Explains the mathematics, theory, and methods of Big Data as applied to finance and investing Examines the mathematics, theory, and practical use of the revolutionary techniques that are transforming the industry. Designed for mathematically-advanced students and discerning financial practitioners alike, this energizing book presents new, cutting-edge content based on world-class research taught in the leading Financial Mathematics and Engineering programs in the world. Marco Avellaneda, a leader in quantitative finance, and quantitative methodology author Irene Aldridge help readers harness the power of Big Data. Provides a complete account of Big Data that includes proofs, step-by-step applications, and code samples Explains the difference between Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) Covers vital topics in the field in a clear, straightforward manner Compares, contrasts, and discusses Big Data and Small Data Includes Cornell University-tested educational materials such as lesson plans, end-of-chapter questions, and downloadable lecture slides
Big Data in Context : Legal, Social and Technological Insights
Sheds new light on a selection of big data scenarios from an interdisciplinary perspective. It features legal, sociological and economic approaches to fundamental big data topics such as privacy, data quality and the ECJ’s Safe Harbor decision on the one hand, and practical applications such as smart cars, wearables and web tracking on the other. Addressing the interests of researchers and practitioners alike, it provides a comprehensive overview of and introduction to the emerging challenges regarding big data.All contributions are based on papers submitted in connection with ABIDA (Assessing Big Data), an interdisciplinary research project exploring the societal aspects of big data and funded by the German Federal Ministry of Education and Research.
Big Data : Conceptual Analysis and Applications
The book is devoted to the analysis of big data in order to extract from these data hidden patterns necessary for making decisions about the rational behavior of complex systems with the different nature that generate this data. To solve these problems, a group of new methods and tools is used, based on the self-organization of computational processes, the use of crisp and fuzzy cluster analysis methods, hybrid neural-fuzzy networks, and others. The book solves various practical problems. In particular, for the tasks of 3D image recognition and automatic speech recognition large-scale neural networks with applications for Deep Learning systems were used.
Big Data : An Art of Decision Making
Manipulating and processing masses of digital data is never a purely technical activity. It requires an interpretative and exploratory outlook - already well known in the social sciences and the humanities - to convey intelligible results from data analysis algorithms and create new knowledge. Big Data is based on an inquiry of several years within Proxem, a software publisher specializing in big data processing. The book examines how data scientists explore, interpret and visualize our digital traces to make sense of them, and to produce new knowledge. Grounded in epistemology and science and technology studies, Big Data offers a reflection on data in general, and on how they help us to better understand reality and decide on our daily actions.
Bidding Strategies in Agent-Based Continuous Double Auctions
Presents a new bidding strategy for agents to adopt in CDAs and propose tools to enhance the performance of existing bidding strategies in CDAs. The superior performance of the new bidding strategy as well as the tools presented in this book are illustrated through extensive experiments.
Benign anorectal diseases : Diagnosis with endoanal and endorectal ultrasound and new treatment options
New three-dimensional endoanal and endorectal ultrasonographic and magnetic resonance imaging techniques have given better insight into the complex anatomy of the pelvic floor and its pathologic modification in benign anorectal diseases. Obstetrical events leading to fecal incontinence in females, the relationship between fistulous tracks and the sphincter complex, and mechanisms of obstructed defecation syndrome can now be accurately evaluated, which is of fundamental importance for decision making.
Being apart from reasons : The role of reasons in public and private moral decision-making
The book presents objections to the most common response given by contemporary legal and political theorists to the moral complexity of decision-making in modern societies, namely: the attempt to release public agents from their argumentative burden by insulating a particular set of reasons from the general.
Behavioral finance and your portfolio : A navigation guide for building wealth
Designed for investors who are serious about maximizing their gains, in this book you’ll discover how to: ● Take control of your decision-making—even when challenging markets push greed and fear to intolerable levels ● Reflect on how to make investment decisions using data-backed and substantiated information instead of emotion and bias ● Counter deep-seated biases like loss aversion, hindsight and overconfidence with self-awareness and hard facts ● Identify your personal investment psychology profile, which you can use to inform your future financial decision making
Beginning Excel What-If Data Analysis tools : Getting started with goal seek, data tables, scenarios, and solver
Excels what-if data analysis tools let you experiment with your data to project future results. In turn, these predictions will lead to better decision making and unlock the mystery of many business analysis scenarios. For example, what-if data analysis tools will enable you to forecast how lowering the price per unitwhile increasing projected unit salesmight affect your profit margins. Beginning Excel What-If Data Analysis Tools explores the use of Goal Seek, Data Tables, Scenarios, and Solver to help you get insight on your data. This book is focused and to the point, and it provides tutorial treatment of what-if tools in a practical, hands-on manner.
Be data literate : The data literacy skills everyone needs to succeed
It is not enough for a business to have the best data if those using it don't understand the right questions to ask or how to use the information generated to make decisions. Be Data Literate is the essential guide to developing the curiosity, creativity and critical thinking necessary to make anyone data literate, without retraining as a data scientist or statistician.
Bayesian networks and Influence diagrams : A guide to construction and analysis
Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, prediction, decision making, classification, troubleshooting, and data mining under uncertainty. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. Intended primarily for practitioners, this book does not require sophisticated mathematical skills or deep understanding of the underlying theory and methods nor does it discuss alternative technologies for reasoning under uncertainty.
Bayesian Networks and Decision Graphs
Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query answering. This includes belief updating, finding the most probable explanation for the observed evidence, detecting conflicts in the evidence entered into the network, determining optimal strategies, analyzing for relevance, and performing sensitivity analysis. The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams.It contians two parts. The first part focuses on probabilistic graphical models. Compared with the previous book, the new edition also includes a thorough description of recent extensions to the Bayesian network modeling language, advances in exact and approximate belief updating algorithms, and methods for learning both the structure and the parameters of a Bayesian network. The second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision processes and partially ordered decision problems.
Basic concepts in medicinal chemistry
Designed to help students incrementally build their knowledge of fundamental concepts of medicinal chemistry and their applications to therapeutic decisions, it is progressively organized. Specific sections of the text have been updated to make potentially confusing concepts easier to understand. Numerous examples and review questions further reinforce learning and analytical skills.
Average-Cost Control of Stochastic Manufacturing Systems
This book is concerned with hierarchical control of manufacturing systems under uncertainty. It focuses on system performance measured in long-run average cost criteria, exploring the relationship between control problems with a discounted cost and that with a long-run average cost in connection with hierarchical control. A new theory is articulated that shows that hierarchical decision making in the context of a goal-seeking manufacturing system can lead to a near optimization of its objective. The approach in the book considers manufacturing systems in which events occur at different time scales.
Autonomous Navigation in Dynamic Environments
The purpose of this book is to address the challenging problem of Autonomous Navigation in Dynamic Environments, and to present new ideas and approaches in this newly emerging technical domain. The book surveys the state-of-the-art, discusses in detail various related challenging technical aspects, and addresses upcoming technologies in this field. The aim of the book is to establish a foundation for a broad class of mobile robot mapping and navigation methodologies for indoor, outdoor, and exploratory missions.Three main topics located on the cutting edge of the state of the art are addressed, from both the theoretical and technological point of views: Dynamic world understanding and modelling for safe navigation, Obstacle avoidance and motion planning in dynamic environments, and Human-robot physical interactions. Several models and approaches are proposed for solving problems such as Simultaneous Localization and Mapping (SLAM) in dynamic environments, Mobile obstacle detection and tracking, World state estimation and motion prediction, Safe navigation in dynamic environments, Motion planning in dynamic environments, Robust decision making under uncertainty, and Human-Robot physical interactions.
