The Quantum Enzyme Code (The Woman who Discovered the Cure for AIDS)
The Quantum Enzyme Code (The Woman who Discovered the Cure for AIDS)

Author: Matthew David Frango

Publisher: iUniverse

Published: 2006-06

Total Pages: 622

ISBN-13: 0595393810

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This novel, part romance, part science fiction, part thriller, is the story of a famous child prodigy in mathematics and music, Dianna Utterson, who, later as a PHD student in biophysics, develops a fool-proof, anti-mutagenic vaccine against AIDS. It's also a story of a jealous medical student's obsession to steal the genetic code and Fourier analysis developed by his lover, Dianna, needed by his future pharmaceutical corporation to manufacture her wonder AIDS drug. The book's most interesting sub-plot is the Jesuit-controlled, Pythagorean secrecy surrounding her cure and its conflict with traditional Vatican theology. With clear allusions to quantum physics, and molecular biology as developed by the American James Watson, and the British Scientists Francis Crick and Rosalind Franklin, this novel is ideal for high school and college-age students, and those readers interested in the magic of bio-medical research in its quest to find cures for mankind's most elusive diseases. It's a lasting work that inspires readers to appreciate science through the uplifting experience of a disarming, beatific heroine, Dianna Utterson. --- Wayne Kappel, Ph.D, recipient of the Distinguished Teacher White House Commission on Presidential Scholars award, 1997

Web-Scale Discovery Services
Web-Scale Discovery Services

Author: Roberto Raieli

Publisher: Chandos Publishing

Published: 2022-03-24

Total Pages: 230

ISBN-13: 0323902995

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Web-Scale Discovery Services: Principles, Applications, Discovery Tools and Development Hypotheses summarizes and presents the state-of-the-art in WSDS. The title promotes a middle-way between finding the best tool for each particular need and the search for the most reliable systems. The title identifies basic theoretical problems and offers practical solutions for librarians. The volume offers a summary of ideas from around the world, giving a new perspective that is backed up by strong theory. Offering a vision for libraries, this book also allows archivists, museum specialists, computer scientists, commercial operators and interested users to deepen their culture and information literacy. The great number of information sources now available and the changing habits of web users has led to the development of Web Scale Discovery Services (WSDS). The goal of these systems and techniques is to make catalogues, databases, institutional repositories, Open Access archives and other databases searchable and discoverable through a single point of access. The diffusion of systems and connections between data disseminated by libraries and published by other institutions poses a challenge to understanding discovery in the modern library. - Lays out the state-of-the-art in WSDS for contemporary libraries and institutions - Presents an innovative take on information retrieval and digital document management - Grounds thinking on a bibliographic basis, combining academic, practical and commercial aspects - Offers a perspective on how WSDS and discovery tools are seen and used internationally - Provides a version of culture and information literacy of relevance to a broad-range of cultural specialists

Pathway Analysis for Drug Discovery
Pathway Analysis for Drug Discovery

Author: Anton Yuryev

Publisher: John Wiley & Sons

Published: 2008-09-17

Total Pages: 332

ISBN-13: 0470399260

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This book introduces drug researchers to the novel computational approaches of pathway analysis and explains the existing applications that can save time and money in the drug discovery process. It covers traditional computational methods and software for pathway analysis microarray, proteomics, and metabolomics. It explains pathway reconstruction of diseases and toxic states, pathway analysis in various phases, dynamic modeling of drug responses, and more. This is a core resource for drug discovery and pharmaceutical industry researchers, chemists, and biologists and for professionals in related fields.

Managing Metadata in Web-scale Discovery Systems
Managing Metadata in Web-scale Discovery Systems

Author: Louise F Spiteri

Publisher: Facet Publishing

Published: 2016-05-27

Total Pages: 209

ISBN-13: 1783300698

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This book shows you how to harness the power of linked data and web-scale discovery systems to manage and link widely varied content across your library collection. Libraries are increasingly using web-scale discovery systems to help clients find a wide assortment of library materials, including books, journal articles, special collections, archival collections, videos, music and open access collections. Depending on the library material catalogued, the discovery system might need to negotiate different metadata standards, such as AACR, RDA, RAD, FOAF, VRA Core, METS, MODS, RDF and more. In Managing Metadata in Web-Scale Discovery Systems, editor Louise Spiteri and a range of international experts show you how to: - maximize the effectiveness of web-scale discovery systems - provide a smooth and seamless discovery experience to your users - help users conduct searches that yield relevant results - manage the sheer volume of items to which you can provide access, so your users can actually find what they need - maintain shared records that reflect the needs, languages, and identities of culturally and ethnically varied communities - manage metadata both within, across, and outside, library discovery tools by converting your library metadata to linked open data that all systems can access - manage user generated metadata from external services such as Goodreads and LibraryThing - mine user generated metadata to better serve your users in areas such as collection development or readers’ advisory. The book will be essential reading for cataloguers, technical services and systems librarians and library and information science students studying modules on metadata, cataloguing, systems design, data management, and digital libraries. The book will also be of interest to those managing metadata in archives, museums and other cultural heritage institutions.

Resource Discovery for the Twenty-First Century Library
Resource Discovery for the Twenty-First Century Library

Author: Simon McLeish

Publisher: Facet Publishing

Published: 2020-06-26

Total Pages: 240

ISBN-13: 1783301384

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Discovery is central to academic activities at all levels and is a major focus for libraries and museums. Of all the parts of modern library provision, discovery services are the most clearly affected by developments in IT, from databases to search engines to linked data to machine learning. It is crucial to the relationship between libraries and their communities. This book will help its readers learn how to adapt in a fast changing area to continue to provide a high level of service. Resource Discovery for the Twenty-First Century Library contains a range of contributions analysing the ways in which libraries are tackling the challenges facing them in discovery in the (post)-Google era. Chapters are written by experts, both global and local – describing specific areas of discovery and local implementations and ideas. The book will help with enhancing discovery both inbound – making locally held resources globally discoverable, and outbound – making global resources locally discoverable, in ways which are relevant to your user community. Content covered includes: · a survey of what resource discovery is today · case studies from around the world of interesting approaches to discovery · analysis of how users approach discovery · how to understand and make the best use of Internet search engines · using limited resources to help users find collections · linked open data and discovery · the future of discovery. This book will be useful for subject librarians and others who give direct support to library users, digital library technicians, managers, staff with responsibility for managing electronic resources, metadata and discovery specialists, trainers and user education specialists. It will also be of use to curators and others who give direct support to researchers, managers of digitisation and cataloguing products, IT staff, trainers and user education specialists.

Advanced Methods for Knowledge Discovery from Complex Data
Advanced Methods for Knowledge Discovery from Complex Data

Author: Ujjwal Maulik

Publisher: Springer Science & Business Media

Published: 2006-05-06

Total Pages: 375

ISBN-13: 1846282845

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The growth in the amount of data collected and generated has exploded in recent times with the widespread automation of various day-to-day activities, advances in high-level scienti?c and engineering research and the development of e?cient data collection tools. This has given rise to the need for automa- callyanalyzingthedatainordertoextractknowledgefromit,therebymaking the data potentially more useful. Knowledge discovery and data mining (KDD) is the process of identifying valid, novel, potentially useful and ultimately understandable patterns from massive data repositories. It is a multi-disciplinary topic, drawing from s- eral ?elds including expert systems, machine learning, intelligent databases, knowledge acquisition, case-based reasoning, pattern recognition and stat- tics. Many data mining systems have typically evolved around well-organized database systems (e.g., relational databases) containing relevant information. But, more and more, one ?nds relevant information hidden in unstructured text and in other complex forms. Mining in the domains of the world-wide web, bioinformatics, geoscienti?c data, and spatial and temporal applications comprise some illustrative examples in this regard. Discovery of knowledge, or potentially useful patterns, from such complex data often requires the - plication of advanced techniques that are better able to exploit the nature and representation of the data. Such advanced methods include, among o- ers, graph-based and tree-based approaches to relational learning, sequence mining, link-based classi?cation, Bayesian networks, hidden Markov models, neural networks, kernel-based methods, evolutionary algorithms, rough sets and fuzzy logic, and hybrid systems. Many of these methods are developed in the following chapters.

Neglected Diseases: Extensive Space for Modern Drug Discovery
Neglected Diseases: Extensive Space for Modern Drug Discovery

Author:

Publisher: Academic Press

Published: 2018-10-19

Total Pages: 242

ISBN-13: 0128151447

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Neglected Diseases: Extensive Space for Modern Drug Discovery provides in-depth reviews on the last progresses about neglected tropical diseases research. Topics covered in this volume include Leishmaniasis, Tripanosomiasis, Onchocerciasis and Ebolavirus infections, with insights on the future of the research on them. Part of the volume is devoted to recent contributions this field received from X-Ray crystallography. - Provides accurate reviews from selected experts on the topic of Neglected Tropical Diseases - Each chapter of the volume provides useful graphic material for ease of reading of the audience - provides the latest insights and future perspectives on the covered neglected diseases

Knowledge Discovery from XML Documents
Knowledge Discovery from XML Documents

Author: Richi Nayak

Publisher: Springer Science & Business Media

Published: 2006-03-23

Total Pages: 113

ISBN-13: 3540331808

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This book constitutes the refereed proceedings of the First International Workshop on Knowledge Discovery from XML Documents, KDXD 2006, held in Singapore in conjunction with the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006). The ten revised full papers presented together with two invited talks were carefully reviewed and selected from 26 submissions. The papers are organized in topical sections.

Discovery Science
Discovery Science

Author: Klaus P. Jantke

Publisher: Springer

Published: 2003-06-30

Total Pages: 510

ISBN-13: 3540456503

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These are the conference proceedings of the 4th International Conference on Discovery Science (DS 2001). Although discovery is naturally ubiquitous in s- ence, and scientific discovery itself has been subject to scientific investigation for centuries, the term Discovery Science is comparably new. It came up in conn- tion with the Japanese Discovery Science project (cf. Arikawa's invited lecture on The Discovery Science Project in Japan in the present volume) some time during the last few years. Setsuo Arikawa is the father in spirit of the Discovery Science conference series. He led the above mentioned project, and he is currently serving as the chairman of the international steering committee for the Discovery Science c- ference series. The other members of this board are currently (in alphabetical order) Klaus P. Jantke, Masahiko Sato, Ayumi Shinohara, Carl H. Smith, and Thomas Zeugmann. Colleagues and friends from all over the world took the opportunity of me- ing for this conference to celebrate Arikawa's 60th birthday and to pay tribute to his manifold contributions to science, in general, and to Learning Theory and Discovery Science, in particular. Algorithmic Learning Theory (ALT, for short) is another conference series initiated by Setsuo Arikawa in Japan in 1990. In 1994, it amalgamated with the conference series on Analogical and Inductive Inference (AII), when ALT was held outside of Japan for the first time.

Advances in Knowledge Discovery and Data Mining
Advances in Knowledge Discovery and Data Mining

Author: Qiang Yang

Publisher: Springer

Published: 2019-04-03

Total Pages: 575

ISBN-13: 3030161420

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The three-volume set LNAI 11439, 11440, and 11441 constitutes the thoroughly refereed proceedings of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019, held in Macau, China, in April 2019. The 137 full papers presented were carefully reviewed and selected from 542 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems, and the emerging applications. They are organized in the following topical sections: classification and supervised learning; text and opinion mining; spatio-temporal and stream data mining; factor and tensor analysis; healthcare, bioinformatics and related topics; clustering and anomaly detection; deep learning models and applications; sequential pattern mining; weakly supervised learning; recommender system; social network and graph mining; data pre-processing and featureselection; representation learning and embedding; mining unstructured and semi-structured data; behavioral data mining; visual data mining; and knowledge graph and interpretable data mining.