Advances in Artificial Intelligence, Computation, and Data Science

Advances in Artificial Intelligence, Computation, and Data Science

Author: Tuan D. Pham

Publisher: Springer Nature

Published: 2021-07-12

Total Pages: 373

ISBN-13: 303069951X

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Artificial intelligence (AI) has become pervasive in most areas of research and applications. While computation can significantly reduce mental efforts for complex problem solving, effective computer algorithms allow continuous improvement of AI tools to handle complexity—in both time and memory requirements—for machine learning in large datasets. Meanwhile, data science is an evolving scientific discipline that strives to overcome the hindrance of traditional skills that are too limited to enable scientific discovery when leveraging research outcomes. Solutions to many problems in medicine and life science, which cannot be answered by these conventional approaches, are urgently needed for society. This edited book attempts to report recent advances in the complementary domains of AI, computation, and data science with applications in medicine and life science. The benefits to the reader are manifold as researchers from similar or different fields can be aware of advanced developments and novel applications that can be useful for either immediate implementations or future scientific pursuit. Features: Considers recent advances in AI, computation, and data science for solving complex problems in medicine, physiology, biology, chemistry, and biochemistry Provides recent developments in three evolving key areas and their complementary combinations: AI, computation, and data science Reports on applications in medicine and physiology, including cancer, neuroscience, and digital pathology Examines applications in life science, including systems biology, biochemistry, and even food technology This unique book, representing research from a team of international contributors, has not only real utility in academia for those in the medical and life sciences communities, but also a much wider readership from industry, science, and other areas of technology and education.


Expert Systems in Chemistry Research

Expert Systems in Chemistry Research

Author: Markus C. Hemmer

Publisher: CRC Press

Published: 2007-12-13

Total Pages: 418

ISBN-13: 1420053248

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Expert systems allow scientists to access, manage, and apply data and specialized knowledge from various disciplines to their own research. Expert Systems in Chemistry Research explains the general scientific basis and computational principles behind expert systems and demonstrates how they can improve the efficiency of scientific workflows


Mathematical Challenges from Theoretical/Computational Chemistry

Mathematical Challenges from Theoretical/Computational Chemistry

Author: Committee on Mathematical Challenges from Computational Chemistry

Publisher: National Academies Press

Published: 1995-04-12

Total Pages: 144

ISBN-13: 0309560640

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Computational methods are rapidly becoming major tools of theoretical, pharmaceutical, materials, and biological chemists. Accordingly, the mathematical models and numerical analysis that underlie these methods have an increasingly important and direct role to play in the progress of many areas of chemistry. This book explores the research interface between computational chemistry and the mathematical sciences. In language that is aimed at non-specialists, it documents some prominent examples of past successful cross-fertilizations between the fields and explores the mathematical research opportunities in a broad cross-section of chemical research frontiers. It also discusses cultural differences between the two fields and makes recommendations for overcoming those differences and generally promoting this interdisciplinary work.


Reviews in Computational Chemistry, Volume 18

Reviews in Computational Chemistry, Volume 18

Author: Kenny B. Lipkowitz

Publisher: John Wiley & Sons

Published: 2003-04-14

Total Pages: 384

ISBN-13: 0471461423

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Seit vielen Jahren praxisbewährt! Auch dieser 18. Band der Reihe Reviews in Computational Chemistry gibt Studenten und Forschern einen Einblick in Rechenverfahren, die sie anwenden wollen, ohne daß die theoretischen Grundlagen zu ihrem Arbeitsgebiet gehören. Das methodische Spektrum umfaßt Molecular Modeling, Quantenchemie, CAMD, QSAR, Molekülmechanik und -dynamik. Mit einem Autoren- und einem Stichwortverzeichnis sowie einer ausführlichen Softwareliste, die Hunderte von Programmen, Dienstleistungen und Anbietern umfaßt.


Knowledge-based Expert Systems in Chemistry

Knowledge-based Expert Systems in Chemistry

Author: Philip Judson

Publisher: Royal Society of Chemistry

Published: 2019-02-07

Total Pages: 298

ISBN-13: 1788014715

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This new edition has been thoroughly revised and updated to reflect the advances in using knowledge-based expert systems for chemistry.


Machine Learning in Chemistry

Machine Learning in Chemistry

Author: Jon Paul Janet

Publisher: American Chemical Society

Published: 2020-05-28

Total Pages: 189

ISBN-13: 0841299005

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Recent advances in machine learning or artificial intelligence for vision and natural language processing that have enabled the development of new technologies such as personal assistants or self-driving cars have brought machine learning and artificial intelligence to the forefront of popular culture. The accumulation of these algorithmic advances along with the increasing availability of large data sets and readily available high performance computing has played an important role in bringing machine learning applications to such a wide range of disciplines. Given the emphasis in the chemical sciences on the relationship between structure and function, whether in biochemistry or in materials chemistry, adoption of machine learning by chemistsderivations where they are important


Reviews in Computational Chemistry, Volume 1

Reviews in Computational Chemistry, Volume 1

Author: Kenny B. Lipkowitz

Publisher: John Wiley & Sons

Published: 2009-09-22

Total Pages: 443

ISBN-13: 0470126051

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This book is an account of current developments in computational chemistry, a new multidisciplinary area of research. Experts in computational chemistry, the editors use and develop techniques for computer-assisted molecular design. The core of the text itself deals with techniques for computer-assisted molecular design. The book is suitable for both beginners and experts. In addition, protocols and software for molecular recognition and the relationship between structure and biological activity of drug molecules are discussed in detail. Each chapter includes a mini-tutorial, as well as discussion of advanced topics. Special Feature: The appendix to this book contains an extensive list of available software for molecular modeling.


Reviews in Computational Chemistry, Volume 7

Reviews in Computational Chemistry, Volume 7

Author: Kenny B. Lipkowitz

Publisher: John Wiley & Sons

Published: 2009-09-22

Total Pages: 441

ISBN-13: 0470126116

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This is the seventh volume in the successful series designed to help the chemistry community keep current with the many new developments in computational techniques. The writing style is refreshingly pedagogical and non-mathematical, allowing students and researchers access to computational methods outside their immediate area of expertise. Each invited author approaches a topic with the aim of helping the reader understand the material, solve problems, and locate key references quickly.


Machine Learning in Chemistry

Machine Learning in Chemistry

Author: Hugh M. Cartwright

Publisher: Royal Society of Chemistry

Published: 2020-07-15

Total Pages: 564

ISBN-13: 1788017897

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Progress in the application of machine learning (ML) to the physical and life sciences has been rapid. A decade ago, the method was mainly of interest to those in computer science departments, but more recently ML tools have been developed that show significant potential across wide areas of science. There is a growing consensus that ML software, and related areas of artificial intelligence, may, in due course, become as fundamental to scientific research as computers themselves. Yet a perception remains that ML is obscure or esoteric, that only computer scientists can really understand it, and that few meaningful applications in scientific research exist. This book challenges that view. With contributions from leading research groups, it presents in-depth examples to illustrate how ML can be applied to real chemical problems. Through these examples, the reader can both gain a feel for what ML can and cannot (so far) achieve, and also identify characteristics that might make a problem in physical science amenable to a ML approach. This text is a valuable resource for scientists who are intrigued by the power of machine learning and want to learn more about how it can be applied in their own field.