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- 588pages
- 21 heures de lecture
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Leading experts illustrate how sophisticated computational data mining techniques can impact contemporary drug discovery and development In the era of post-genomic drug development, extracting and applying knowledge from chemical, biological, and clinical data is one of the greatest challenges facing the pharmaceutical industry. Pharmaceutical Data Mining brings together contributions from leading academic and industrial scientists, who address both the implementation of new data mining technologies and application issues in the industry. This accessible, comprehensive collection discusses important theoretical and practical aspects of pharmaceutical data mining, focusing on diverse approaches for drug discovery―including chemogenomics, toxicogenomics, and individual drug response prediction. The five main sections of this volume In one concentrated reference, Pharmaceutical Data Mining reveals the role and possibilities of these sophisticated techniques in contemporary drug discovery and development. It is ideal for graduate-level courses covering pharmaceutical science, computational chemistry, and bioinformatics. In addition, it provides insight to pharmaceutical scientists, principal investigators, principal scientists, research directors, and all scientists working in the field of drug discovery and development and associated industries.
Achat du livre
Pharmaceutical Data Mining, Konstantin V. Balakin, Sean Ekins
- Langue
- Année de publication
- 2009
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- (rigide)
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- Titre
- Pharmaceutical Data Mining
- Sous-titre
- Approaches and Applications for Drug Discovery
- Langue
- Anglais
- Auteurs
- Konstantin V. Balakin, Sean Ekins
- Éditeur
- Wiley
- Publié
- 2009
- Format
- rigide
- Pages
- 588
- ISBN10
- 0470196084
- ISBN13
- 9780470196083
- Séries
- Mots clés
- Nonfiction, Science et Mathématiques, Sciences naturelles, Littérature contemporaine, Technologie, Médecine, Chimie, Littérature spécialisée, Connaissances et savoir, Industrie, Pharmacologie, Scientifiques, Pharmacie, Médicaments, remèdes
- Description
- Leading experts illustrate how sophisticated computational data mining techniques can impact contemporary drug discovery and development In the era of post-genomic drug development, extracting and applying knowledge from chemical, biological, and clinical data is one of the greatest challenges facing the pharmaceutical industry. Pharmaceutical Data Mining brings together contributions from leading academic and industrial scientists, who address both the implementation of new data mining technologies and application issues in the industry. This accessible, comprehensive collection discusses important theoretical and practical aspects of pharmaceutical data mining, focusing on diverse approaches for drug discovery―including chemogenomics, toxicogenomics, and individual drug response prediction. The five main sections of this volume In one concentrated reference, Pharmaceutical Data Mining reveals the role and possibilities of these sophisticated techniques in contemporary drug discovery and development. It is ideal for graduate-level courses covering pharmaceutical science, computational chemistry, and bioinformatics. In addition, it provides insight to pharmaceutical scientists, principal investigators, principal scientists, research directors, and all scientists working in the field of drug discovery and development and associated industries.