Ying Ding – författare

Written by experts that include originators of some key ideas, chapters in the Handbook of Multiple Testing cover multiple comparison problems big and small, with guidance toward error rate control and insights on how principles developed earlier can be applied to current and emerging problems. Some highlights of the coverages are as follows.Error rate control is useful for controlling the incorrect decision rate. Chapter 1 introduces Tukey's original multiple comparison error rates and point to how they have been applied and adapted to modern multiple comparison problems as discussed in the later chapters. Principles endure. While the closed testing principle is more familiar, Chapter 4 shows the partitioning principle can derive confidence sets for multiple tests, which may become important as the profession goes beyond making decisions based on p-values.Multiple comparisons of treatment efficacy often involve multiple doses and endpoints. Chapter 12 on multiple endpoints explains how different choices of endpoint types lead to different multiplicity adjustment strategies, while Chapter 11 on the MCP-Mod approach is particularly useful for dose-finding. To assess efficacy in clinical trials with multiple doses and multiple endpoints, the reader can see the traditional approach in Chapter 2, the Graphical approach in Chapter 5, and the multivariate approach in Chapter 3. Personalized/precision medicine based on targeted therapies, already a reality, naturally leads to analysis of efficacy in subgroups. Chapter 13 draws attention to subtle logical issues in inferences on subgroups and their mixtures, with a principled solution that resolves these issues. This chapter has implication toward meeting the ICHE9R1 Estimands requirement.Besides the mere multiple testing methodology itself, the handbook also covers related topics like the statistical task of model selection in Chapter 7 or the estimation of the proportion of true null hypotheses (or, in other words, the signal prevalence) in Chapter 8. It also contains decision-theoretic considerations regarding the admissibility of multiple tests in Chapter 6. The issue of selected inference is addressed in Chapter 9.Comparison of responses can involve millions of voxels in medical imaging or SNPs in genome-wide association studies (GWAS). Chapter 14 and Chapter 15 provide state of the art methods for large scale simultaneous inference in these settings.

Written by experts that include originators of some key ideas, chapters in the Handbook of Multiple Testing cover multiple comparison problems big and small, with guidance toward error rate control and insights on how principles developed earlier can be applied to current and emerging problems. Some highlights of the coverages are as follows.Error rate control is useful for controlling the incorrect decision rate. Chapter 1 introduces Tukey's original multiple comparison error rates and point to how they have been applied and adapted to modern multiple comparison problems as discussed in the later chapters. Principles endure. While the closed testing principle is more familiar, Chapter 4 shows the partitioning principle can derive confidence sets for multiple tests, which may become important as the profession goes beyond making decisions based on p-values.Multiple comparisons of treatment efficacy often involve multiple doses and endpoints. Chapter 12 on multiple endpoints explains how different choices of endpoint types lead to different multiplicity adjustment strategies, while Chapter 11 on the MCP-Mod approach is particularly useful for dose-finding. To assess efficacy in clinical trials with multiple doses and multiple endpoints, the reader can see the traditional approach in Chapter 2, the Graphical approach in Chapter 5, and the multivariate approach in Chapter 3. Personalized/precision medicine based on targeted therapies, already a reality, naturally leads to analysis of efficacy in subgroups. Chapter 13 draws attention to subtle logical issues in inferences on subgroups and their mixtures, with a principled solution that resolves these issues. This chapter has implication toward meeting the ICHE9R1 Estimands requirement.Besides the mere multiple testing methodology itself, the handbook also covers related topics like the statistical task of model selection in Chapter 7 or the estimation of the proportion of true null hypotheses (or, in other words, the signal prevalence) in Chapter 8. It also contains decision-theoretic considerations regarding the admissibility of multiple tests in Chapter 6. The issue of selected inference is addressed in Chapter 9.Comparison of responses can involve millions of voxels in medical imaging or SNPs in genome-wide association studies (GWAS). Chapter 14 and Chapter 15 provide state of the art methods for large scale simultaneous inference in these settings.

Internet and web technology penetrates many aspects of our daily life. Its importance as a medium for business transactions will grow exponentially during the next few years. In terms of the involved market volume, the B2B area will hereby be the most interesting area. Also, it will be the place where the new technology will lead to drastic changes in established customer relationships and business models. In an era where open and flexible electronic commerce provides new types of services to its users, simple 1-1 connections will be replaced by n-m relationships between customers and vendors. This new flexibility in electronic trading will generate serious challenges. The main problem stems from the heterogeneity of information descriptions used by vendors and customers, creating problems in both manual trading and in direct 1-1 electronic trading. In the case of B2B market places, it becomes too serious to be neglected. Product descriptions, catalog formats and business documents are often unstructured and non-standardized.Intelligent solutions that mechanize the structuring, standardizing, aligning, and personalizing process are a key requisite for successfully overcoming the current bottlenecks of B2B electronic commerce while enabling its further growth. This text discusses the main problems of information integration in this area and sketches several technological solution paths.

Internet and web technology penetrates many aspects of our daily life. Its importance as a medium for business transactions will grow exponentially during the next few years. In terms of the involved market volume, the B2B area will hereby be the most interesting area. Also, it will be the place, where the new technology will lead to drastic changes in established customer relationships and business models. In an era where open and flexible electronic commerce provides new types of services to its users, simple 1-1 connections will be replaced by n-m relationships between customers and vendors. This new flexibility in electronic trading will generate serious challenges. The main problem stems from the heterogeneity of information descriptions used by vendors and customers, creating problems in both manual trading and in direct 1-1 electronic trading. In the case of B2B market places, it becomes too serious to be neglected. Product descriptions, catalog formats and business documents are often unstructured and non-standardized. Intelligent solutions that mechanize the structuring, standardizing, aligning, and personalizing process are a key requisite for successfully overcoming the current bottlenecks of B2B electronic commerce while enabling its further growth. Intelligent Information Integration in B2B Electronic Commerce discusses the main problems of information integration in this area and sketches several technological solution paths. Intelligent Information Integration in B2B Electronic Commerce is designed to meet the needs of a professional audience composed of researchers and practitioners in industry and graduate level students in Computer Science.

This book constitutes the proceedings of the 20th International Semantic Web Conference, ISWC 2021, which took place in October 2021.

Table of Contents: Scholarly Networking Needs and Desires / The VIVO Ontology / Implementing VIVO and Filling It with Life / Case Study: University of Colorado at Boulder / Case Study: Weill Cornell Medical College / Extending VIVO / Analyzing and Visualizing VIVO Data / The Future of VIVO: Growing the Community

The current drug development paradigm---sometimes expressed as, ``One disease, one target, one drug''---is under question, as relatively few drugs have reached the market in the last two decades. Meanwhile, the research focus of drug discovery is being placed on the study of drug action on biological systems as a whole, rather than on individual components of such systems. The vast amount of biological information about genes and proteins and their modulation by small molecules is pushing drug discovery to its next critical steps, involving the integration of chemical knowledge with these biological databases. Systematic integration of these heterogeneous datasets and the provision of algorithms to mine the integrated datasets would enable investigation of the complex mechanisms of drug action; however, traditional approaches face challenges in the representation and integration of multi-scale datasets, and in the discovery of underlying knowledge in the integrated datasets. The Semantic Web, envisioned to enable machines to understand and respond to complex human requests and to retrieve relevant, yet distributed, data, has the potential to trigger system-level chemical-biological innovations. Chem2Bio2RDF is presented as an example of utilizing Semantic Web technologies to enable intelligent analyses for drug discovery.Table of Contents: Introduction / Data Representation and Integration Using RDF / Data Representation and Integration Using OWL / Finding Complex Biological Relationships in PubMed Articles using Bio-LDA / Integrated Semantic Approach for Systems Chemical Biology Knowledge Discovery / Semantic Link Association Prediction / Conclusions / References / Authors' Biographies

This book constitutes the proceedings of the 5th International Conference on Social Informatics, SocInfo 2013, held in Kyoto, Japan, in November 2013. The 23 full papers, 15 short papers and three poster papers included in this volume were carefully reviewed and selected from 103 submissions. The papers present original research work on studying the interplay between socially-centric platforms and social phenomena.

This book constitutes the proceedings of the Third Joint International Semantic Technology Conference, JIST 2013, held in Seoul, South Korea, in November 2013.The 32 papers, included four tutorials and 5 workshop papers, in this volume were carefully reviewed and selected from numerous submissions. The contributions are organized in topical sections on semantic Web services, multilingual issues, biomedical applications, ontology construction, semantic reasoning, semantic search and query, ontology mapping, and learning and discovery.

This book is an authoritative handbook of current topics, technologies and methodological approaches that may be used for the study of scholarly impact. The included methods cover a range of fields such as statistical sciences, scientific visualization, network analysis, text mining, and information retrieval. The techniques and tools enable researchers to investigate metric phenomena and to assess scholarly impact in new ways. Each chapter offers an introduction to the selected topic and outlines how the topic, technology or methodological approach may be applied to metrics-related research. Comprehensive and up-to-date, Measuring Scholarly Impact: Methods and Practice is designed for researchers and scholars interested in informetrics, scientometrics, and text mining. The hands-on perspective is also beneficial to advanced-level students in fields from computer science and statistics to information science.

This book constitutes the refereed proceedings of Satellite Events held at the 19th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2014 in November 2014.

This book is an authoritative handbook of current topics, technologies and methodological approaches that may be used for the study of scholarly impact. The included methods cover a range of fields such as statistical sciences, scientific visualization, network analysis, text mining, and information retrieval. The techniques and tools enable researchers to investigate metric phenomena and to assess scholarly impact in new ways. Each chapter offers an introduction to the selected topic and outlines how the topic, technology or methodological approach may be applied to metrics-related research. Comprehensive and up-to-date, Measuring Scholarly Impact: Methods and Practice is designed for researchers and scholars interested in informetrics, scientometrics, and text mining. The hands-on perspective is also beneficial to advanced-level students in fields from computer science and statistics to information science.