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Pharmaceutical Data Mining
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Book Synopsis Pharmaceutical Data Mining by : Konstantin V. Balakin
Download or read book Pharmaceutical Data Mining written by Konstantin V. Balakin and published by John Wiley & Sons. This book was released on 2009-11-19 with total page 584 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 cover: A general overview of the discipline, from its foundations to contemporary industrial applications Chemoinformatics-based applications Bioinformatics-based applications Data mining methods in clinical development Data mining algorithms, technologies, and software tools, with emphasis on advanced algorithms and software that are currently used in the industry or represent promising approaches 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.
Book Synopsis Medical Data Mining and Knowledge Discovery by : Krzysztof J. Cios
Download or read book Medical Data Mining and Knowledge Discovery written by Krzysztof J. Cios and published by Physica. This book was released on 2001-01-12 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern medicine generates, almost daily, huge amounts of heterogeneous data. For example, medical data may contain SPECT images, signals like ECG, clinical information like temperature, cholesterol levels, etc., as well as the physician's interpretation. Those who deal with such data understand that there is a widening gap between data collection and data comprehension. Computerized techniques are needed to help humans address this problem. This volume is devoted to the relatively young and growing field of medical data mining and knowledge discovery. As more and more medical procedures employ imaging as a preferred diagnostic tool, there is a need to develop methods for efficient mining in databases of images. Other significant features are security and confidentiality concerns. Moreover, the physician's interpretation of images, signals, or other technical data, is written in unstructured English which is very difficult to mine. This book addresses all these specific features.
Book Synopsis Data Mining in Drug Discovery by : Rémy D. Hoffmann
Download or read book Data Mining in Drug Discovery written by Rémy D. Hoffmann and published by John Wiley & Sons. This book was released on 2013-09-25 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written for drug developers rather than computer scientists, this monograph adopts a systematic approach to mining scientifi c data sources, covering all key steps in rational drug discovery, from compound screening to lead compound selection and personalized medicine. Clearly divided into four sections, the first part discusses the different data sources available, both commercial and non-commercial, while the next section looks at the role and value of data mining in drug discovery. The third part compares the most common applications and strategies for polypharmacology, where data mining can substantially enhance the research effort. The final section of the book is devoted to systems biology approaches for compound testing. Throughout the book, industrial and academic drug discovery strategies are addressed, with contributors coming from both areas, enabling an informed decision on when and which data mining tools to use for one's own drug discovery project.
Book Synopsis Application of Data Mining in Pharmaceutical Research by : Zhenyu Pan
Download or read book Application of Data Mining in Pharmaceutical Research written by Zhenyu Pan and published by Frontiers Media SA. This book was released on 2024-03-07 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Multivariate Analysis in the Pharmaceutical Industry by : Ana Patricia Ferreira
Download or read book Multivariate Analysis in the Pharmaceutical Industry written by Ana Patricia Ferreira and published by Academic Press. This book was released on 2018-04-24 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multivariate Analysis in the Pharmaceutical Industry provides industry practitioners with guidance on multivariate data methods and their applications over the lifecycle of a pharmaceutical product, from process development, to routine manufacturing, focusing on the challenges specific to each step. It includes an overview of regulatory guidance specific to the use of these methods, along with perspectives on the applications of these methods that allow for testing, monitoring and controlling products and processes. The book seeks to put multivariate analysis into a pharmaceutical context for the benefit of pharmaceutical practitioners, potential practitioners, managers and regulators. Users will find a resources that addresses an unmet need on how pharmaceutical industry professionals can extract value from data that is routinely collected on products and processes, especially as these techniques become more widely used, and ultimately, expected by regulators. Targets pharmaceutical industry practitioners and regulatory staff by addressing industry specific challenges Includes case studies from different pharmaceutical companies and across product lifecycle of to introduce readers to the breadth of applications Contains information on the current regulatory framework which will shape how multivariate analysis (MVA) is used in years to come
Book Synopsis The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry by : Stephanie K. Ashenden
Download or read book The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry written by Stephanie K. Ashenden and published by Academic Press. This book was released on 2021-04-23 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Era of Artificial Intelligence, Machine Learning and Data Science in the Pharmaceutical Industry examines the drug discovery process, assessing how new technologies have improved effectiveness. Artificial intelligence and machine learning are considered the future for a wide range of disciplines and industries, including the pharmaceutical industry. In an environment where producing a single approved drug costs millions and takes many years of rigorous testing prior to its approval, reducing costs and time is of high interest. This book follows the journey that a drug company takes when producing a therapeutic, from the very beginning to ultimately benefitting a patient’s life. This comprehensive resource will be useful to those working in the pharmaceutical industry, but will also be of interest to anyone doing research in chemical biology, computational chemistry, medicinal chemistry and bioinformatics. Demonstrates how the prediction of toxic effects is performed, how to reduce costs in testing compounds, and its use in animal research Written by the industrial teams who are conducting the work, showcasing how the technology has improved and where it should be further improved Targets materials for a better understanding of techniques from different disciplines, thus creating a complete guide
Book Synopsis Data Mining in Medical and Biological Research by : Eugenia Giannopoulou
Download or read book Data Mining in Medical and Biological Research written by Eugenia Giannopoulou and published by BoD – Books on Demand. This book was released on 2008-11-01 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book intends to bring together the most recent advances and applications of data mining research in the promising areas of medicine and biology from around the world. It consists of seventeen chapters, twelve related to medical research and five focused on the biological domain, which describe interesting applications, motivating progress and worthwhile results. We hope that the readers will benefit from this book and consider it as an excellent way to keep pace with the vast and diverse advances of new research efforts.
Book Synopsis Computer Applications in Pharmaceutical Research and Development by : Sean Ekins
Download or read book Computer Applications in Pharmaceutical Research and Development written by Sean Ekins and published by John Wiley & Sons. This book was released on 2006-07-11 with total page 840 pages. Available in PDF, EPUB and Kindle. Book excerpt: A unique, holistic approach covering all functions and phases of pharmaceutical research and development While there are a number of texts dedicated to individual aspects of pharmaceutical research and development, this unique contributed work takes a holistic and integrative approach to the use of computers in all phases of drug discovery, development, and marketing. It explains how applications are used at various stages, including bioinformatics, data mining, predicting human response to drugs, and high-throughput screening. By providing a comprehensive view, the book offers readers a unique framework and systems perspective from which they can devise strategies to thoroughly exploit the use of computers in their organizations during all phases of the discovery and development process. Chapters are organized into the following sections: * Computers in pharmaceutical research and development: a general overview * Understanding diseases: mining complex systems for knowledge * Scientific information handling and enhancing productivity * Computers in drug discovery * Computers in preclinical development * Computers in development decision making, economics, and market analysis * Computers in clinical development * Future applications and future development Each chapter is written by one or more leading experts in the field and carefully edited to ensure a consistent structure and approach throughout the book. Figures are used extensively to illustrate complex concepts and multifaceted processes. References are provided in each chapter to enable readers to continue investigating a particular topic in depth. Finally, tables of software resources are provided in many of the chapters. This is essential reading for IT professionals and scientists in the pharmaceutical industry as well as researchers involved in informatics and ADMET, drug discovery, and technology development. The book's cross-functional, all-phases approach provides a unique opportunity for a holistic analysis and assessment of computer applications in pharmaceutics.
Book Synopsis Biological Data Mining and Its Applications in Healthcare by : Xiaoli Li
Download or read book Biological Data Mining and Its Applications in Healthcare written by Xiaoli Li and published by World Scientific. This book was released on 2013-11-28 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biologists are stepping up their efforts in understanding the biological processes that underlie disease pathways in the clinical contexts. This has resulted in a flood of biological and clinical data from genomic and protein sequences, DNA microarrays, protein interactions, biomedical images, to disease pathways and electronic health records. To exploit these data for discovering new knowledge that can be translated into clinical applications, there are fundamental data analysis difficulties that have to be overcome. Practical issues such as handling noisy and incomplete data, processing compute-intensive tasks, and integrating various data sources, are new challenges faced by biologists in the post-genome era. This book will cover the fundamentals of state-of-the-art data mining techniques which have been designed to handle such challenging data analysis problems, and demonstrate with real applications how biologists and clinical scientists can employ data mining to enable them to make meaningful observations and discoveries from a wide array of heterogeneous data from molecular biology to pharmaceutical and clinical domains. Contents:Sequence Analysis:Mining the Sequence Databases for Homology Detection: Application to Recognition of Functions of Trypanosoma brucei brucei Proteins and Drug Targets (G Ramakrishnan, V S Gowri, R Mudgal, N R Chandra and N Srinivasan)Identification of Genes and Their Regulatory Regions Based on Multiple Physical and Structural Properties of a DNA Sequence (Xi Yang, Nancy Yu Song and Hong Yan)Mining Genomic Sequence Data for Related Sequences Using Pairwise Statistical Significance (Yuhong Zhang and Yunbo Rao)Biological Network Mining:Indexing for Similarity Queries on Biological Networks (Günhan Gülsoy, Md Mahmudul Hasan, Yusuf Kavurucu and Tamer Kahveci)Theory and Method of Completion for a Boolean Regulatory Network Using Observed Data (Takeyuki Tamura and Tatsuya Akutsu)Mining Frequent Subgraph Patterns for Classifying Biological Data (Saeed Salem)On the Integration of Prior Knowledge in the Inference of Regulatory Networks (Catharina Olsen, Benjamin Haibe-Kains, John Quackenbush and Gianluca Bontempi)Classification, Trend Analysis and 3D Medical Images:Classification and Its Application to Drug-Target Prediction (Jian-Ping Mei, Chee-Keong Kwoh, Peng Yang and Xiao-Li Li)Characterization and Prediction of Human Protein-Protein Interactions (Yi Xiong, Dan Syzmanski and Daisuke Kihara)Trend Analysis (Wen-Chuan Xie, Miao He and Jake Yue Chen)Data Acquisition and Preprocessing on Three Dimensional Medical Images (Yuhua Jiao, Liang Chen and Jin Chen)Text Mining and Its Biomedical Applications:Text Mining in Biomedicine and Healthcare (Hong-Jie Dai, Chi-Yang Wu, Richard Tzong-Han Tsai and Wen-Lian Hsu)Learning to Rank Biomedical Documents with Only Positive and Unlabeled Examples: A Case Study (Mingzhu Zhu, Yi-Fang Brook Wu, Meghana Samir Vasavada and Jason T L Wang)Automated Mining of Disease-Specific Protein Interaction Networks Based on Biomedical Literature (Rajesh Chowdhary, Boris R Jankovic, Rachel V Stankowski, John A C Archer, Xiangliang Zhang, Xin Gao, Vladimir B Bajic) Readership: Students, professionals, those who perform biological, medical and bioinformatics research. Keywords:Healthcare;Data Mining;Biological Data Mining;Protein Interactions;Gene Regulation;Text Mining;Biological Literature Mining;Drug Discovery;Disease Network;Biological Network;Graph Mining;Sequence Analysis;Structure Analysis;Trend Analysis;Medical ImagesKey Features:Each chapter of this book will include a section to introduce a specific class of data mining techniques, which will be written in a tutorial style so that even non-computational readers such as biologists and healthcare researchers can appreciate themThe book will disseminate the impact research results and best practices of data mining approaches to the cross-disciplinary researchers and practitioners from both the data mining disciplines and the life sciences domains. The authors of the book will be well-known data mining experts, bioinformaticians and cliniciansEach chapter will also provide a detailed description on how to apply the data mining techniques in real-world biological and clinical applications. Thus, readers of this book can easily appreciate the computational techniques and how they can be used to address their own research issues
Book Synopsis Biomedical Data Mining for Information Retrieval by : Sujata Dash
Download or read book Biomedical Data Mining for Information Retrieval written by Sujata Dash and published by John Wiley & Sons. This book was released on 2021-08-24 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: BIOMEDICAL DATA MINING FOR INFORMATION RETRIEVAL This book not only emphasizes traditional computational techniques, but discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications. Biomedical Data Mining for Information Retrieval comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and health informatics is an emerging field of research at the intersection of information science, computer science, and healthcare and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient’s data, electronic health records (EHRs) and lifestyle. Previously, it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical image mining, a novel research area, due to the vast amount of available biomedical images, increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients’ biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions relating to healthcare. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients. Audience Researchers in various fields including computer science, medical informatics, healthcare IOT, artificial intelligence, machine learning, image processing, clinical big data analytics.
Book Synopsis Healthcare Data Analytics by : Chandan K. Reddy
Download or read book Healthcare Data Analytics written by Chandan K. Reddy and published by CRC Press. This book was released on 2015-06-23 with total page 756 pages. Available in PDF, EPUB and Kindle. Book excerpt: At the intersection of computer science and healthcare, data analytics has emerged as a promising tool for solving problems across many healthcare-related disciplines. Supplying a comprehensive overview of recent healthcare analytics research, Healthcare Data Analytics provides a clear understanding of the analytical techniques currently available
Book Synopsis Data Mining and Medical Knowledge Management by : Petr Berka
Download or read book Data Mining and Medical Knowledge Management written by Petr Berka and published by IGI Global Snippet. This book was released on 2009 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: The healthcare industry produces a constant flow of data, creating a need for deep analysis of databases through data mining tools and techniques resulting in expanded medical research, diagnosis, and treatment. ""Data Mining and Medical Knowledge Management: Cases and Applications"" presents case studies on applications of various modern data mining methods in several important areas of medicine, covering classical data mining methods, elaborated approaches related to mining in electroencephalogram and electrocardiogram data, and methods related to mining in genetic data. A premier resource for those involved in data mining and medical knowledge management, this book tackles ethical issues related to cost-sensitive learning in medicine and produces theoretical contributions concerning general problems of data, information, knowledge, and ontologies.
Book Synopsis Medical Informatics by : Hsinchun Chen
Download or read book Medical Informatics written by Hsinchun Chen and published by Springer Science & Business Media. This book was released on 2006-07-19 with total page 656 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensively presents the foundations and leading application research in medical informatics/biomedicine. The concepts and techniques are illustrated with detailed case studies. Authors are widely recognized professors and researchers in Schools of Medicine and Information Systems from the University of Arizona, University of Washington, Columbia University, and Oregon Health & Science University. Related Springer title, Shortliffe: Medical Informatics, has sold over 8000 copies The title will be positioned at the upper division and graduate level Medical Informatics course and a reference work for practitioners in the field.
Book Synopsis Big Data for Big Pharma by : Malena Johannes
Download or read book Big Data for Big Pharma written by Malena Johannes and published by ibidem-Verlag / ibidem Press. This book was released on 2016-06-16 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: Raising costs, ever-increasing regulatory hurdles, and mounting difficulties in finding the next blockbuster drug are just a few of the challenges Big Pharma currently has to face for its research and development process, the heart of its risky business. Big Data claims to be full of insights that Big Pharma need to find a way of harvesting, which could lead to new compounds. Academics, Big Data start-ups, and pharmaceutical companies have focused their research efforts on analytic tools and data technologies to store, collect, analyse, and extract these insights from massive data sets. However, the key question is whether the Big Data hype really does have the claimed accelerating effect on the complex research and development process or if it actually creates another hurdle for Big Pharma innovation. Malena Johannes' timely book sheds light on this question by examining the top 5 pharmaceutical companies and provides an overview on the status quo of Big Data applications within the pharmaceutical industry.
Book Synopsis Data Mining Applications in Engineering and Medicine by : Adem Karahoca
Download or read book Data Mining Applications in Engineering and Medicine written by Adem Karahoca and published by BoD – Books on Demand. This book was released on 2012-08-29 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining Applications in Engineering and Medicine targets to help data miners who wish to apply different data mining techniques. Data mining generally covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, etc. In this book, most of the areas are covered by describing different applications. This is why you will find here why and how Data Mining can also be applied to the improvement of project management. Since Data Mining has been widely used in a medical field, this book contains different chapters reffering to some aspects and importance of its use in the mentioned field: Incorporating Domain Knowledge into Medical Image Mining, Data Mining Techniques in Pharmacovigilance, Electronic Documentation of Clinical Pharmacy Interventions in Hospitals etc. We hope that this book will inspire readers to pursue education and research in this emerging field.
Book Synopsis Handbook of Research on Data Science for Effective Healthcare Practice and Administration by : Noughabi, Elham Akhond Zadeh
Download or read book Handbook of Research on Data Science for Effective Healthcare Practice and Administration written by Noughabi, Elham Akhond Zadeh and published by IGI Global. This book was released on 2017-07-20 with total page 545 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data science has always been an effective way of extracting knowledge and insights from information in various forms. One industry that can utilize the benefits from the advances in data science is the healthcare field. The Handbook of Research on Data Science for Effective Healthcare Practice and Administration is a critical reference source that overviews the state of data analysis as it relates to current practices in the health sciences field. Covering innovative topics such as linear programming, simulation modeling, network theory, and predictive analytics, this publication is recommended for all healthcare professionals, graduate students, engineers, and researchers that are seeking to expand their knowledge of efficient techniques for information analysis in the healthcare professions.
Book Synopsis Advanced Data Mining and Applications by : Xue Li
Download or read book Advanced Data Mining and Applications written by Xue Li and published by Springer Science & Business Media. This book was released on 2006-07-26 with total page 1130 pages. Available in PDF, EPUB and Kindle. Book excerpt: Here are the proceedings of the 2nd International Conference on Advanced Data Mining and Applications, ADMA 2006, held in Xi'an, China, August 2006. The book presents 41 revised full papers and 74 revised short papers together with 4 invited papers. The papers are organized in topical sections on association rules, classification, clustering, novel algorithms, multimedia mining, sequential data mining and time series mining, web mining, biomedical mining, advanced applications, and more.
