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Fundamentals of Clinical Data Science
This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.
Systems Biology
Growth in the pharmaceutical market has slowed down – almost to a standstill. One reason is that governments and other payers are cutting costs in a faltering world economy. But a more fundamental problem is the failure of major companies to discover, develop and market new drugs. Major drugs losing patent protection or being withdrawn from the market are simply not being replaced by new therapies – the pharmaceutical market model is no longer functioning effectively and most pharmaceutical companies are failing to produce the innovation needed for success. This multi-authored new book looks at a vital strategy which can bring innovation to a market in need of new ideas and new products:...
Beyond the Flow
In the wake of the so-called digital revolution numerous attempts have been made to rethink and redesign what scholarly publications can or should be. Beyond the Flow examines the technologies as well as narratives driving this unfolding transformation. However, facing challenges such as the serial crisis, knowledge burying or sudoku research the discourses and practices of scholarly publishing today are mainly shaped by confusion, heterogeneity and uncertainty. By critically interrogating the current state of digital publishing in academia the book asks for how a sustainable post-digital publishing ecology can be imagined.
Library
Two of Canada’s most famous visual artists take on the book medium in their own hilarious way Library is a collection of paintings by two of Canada’s most influential contemporary artists, Michael Dumontier and Neil Farber. From the simple premise of the book title comes a series of images that are laugh-out-loud funny. A collection of book covers adorned with titles painted in simple handwritten fonts are displayed on brightly colored hardboard. Each book forms part of an ongoing series Dumontier and Farber started in 2009. In Dumontier and Farber’s Library, titles like I Lost the Human Race, Change Your Relationship to Your Unchangeable Past, and I Have a Medical Condition That Makes...
Metadata
Zeng and Qin's thorough revision of their benchmark text offers a comprehensive look at the metadata schemas that exist in the world of library and information science and beyond, as well as the contexts in which they operate.
Biocomputing 2007
The Pacific Symposium on Biocomputing (PSB) 2007 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2007 will be held January 3-7, 2007 at the Grand Wailea, Maui. Tutorials will be offered prior to the start of the conference.PSB 2007 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the pr...
The Semantic Web – ISWC 2017
The two-volume set LNCS 10587 + 10588 constitutes the refereed proceedings of the 16th International Semantic Web Conference, ISWC 2017, held in Vienna, Austria, in October 2017. ISWC 2017 is the premier international forum, for the Semantic Web / Linked Data Community. The total of 55 full and 21 short papers presented in this volume were carefully reviewed and selected from 300 submissions. They are organized according to the tracks that were held: Research Track; Resource Track; and In-Use Track.
Ontology Engineering
This book constitutes the thoroughly refereed post-workshop proceedings of the 12th OWL: Experiences and Directions Workshop, OWLED 2015, held in Bethlehem, PA, USA, in October 2015, co-located with ISWC 2015, the International Semantic Web Conference. The 18 revised papers presented were carefully reviewed and selected from 35 initial submissions. Bridging the gap between ontology engineering practices and software engineering, the papers describe reuse methods employed throughout the ontology development cycle; modeling / terminological decisions, alignment and comparison between ontologies, how ontologies are stored, versioned, distributed, and consumed over the Web.
Reasoning Techniques for the Web of Data
Linked Data publishing has brought about a novel “Web of Data”: a wealth of diverse, interlinked, structured data published on the Web. These Linked Datasets are described using the Semantic Web standards and are openly available to all, produced by governments, businesses, communities and academia alike. However, the heterogeneity of such data – in terms of how resources are described and identified – poses major challenges to potential consumers. Herein, we examine use cases for pragmatic, lightweight reasoning techniques that leverage Web vocabularies (described in RDFS and OWL) to better integrate large scale, diverse, Linked Data corpora. We take a test corpus of 1.1 billion RDF...
Semantic AI in Knowledge Graphs
Recent combinations of semantic technology and artificial intelligence (AI) present new techniques to build intelligent systems that identify more precise results. Semantic AI in Knowledge Graphs locates itself at the forefront of this novel development, uncovering the role of machine learning to extend the knowledge graphs by graph mapping or corpus-based ontology learning. Securing efficient results via the combination of symbolic AI and statistical AI such as entity extraction based on machine learning, text mining methods, semantic knowledge graphs, and related reasoning power, this book is the first of its kind to explore semantic AI and knowledge graphs. A range of topics are covered, from neuro-symbolic AI, explainable AI and deep learning to knowledge discovery and mining, and knowledge representation and reasoning. A trailblazing exploration of semantic AI in knowledge graphs, this book is a significant contribution to both researchers in the field of AI and data mining as well as beginner academicians.
