The First Book to Describe the Technical and Practical Elements of Chemical Text MiningExplores the development of chemical structure extraction capabilities and how to incorporate these technologies in daily research workFor scientific researchers, finding too much information on a subject, not finding enough information, or not being able&nb

This book, Chemistry and Industrial Techniques for Chemical Engineers, brings together innovative research, new concepts, and novel developments in the application of new tools for chemical and materials engineers. It contains significant research, reporting new methodologies, and important applications in the fields of chemical engineering as well as the latest coverage of chemical databases and the development of new methods and efficient approaches for chemists. With clear explanations, real-world examples, this volume emphasizes the concepts essential to the practice of chemical science, engineering, and technology while introducing the newest innovations in the field.

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.

Managing and Mining Graph Data is a comprehensive survey book in graph management and mining. It contains extensive surveys on a variety of important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by well known researchers in the field, and provide a broad perspective of the area. This is the first comprehensive survey book in the emerging topic of graph data processing. Managing and Mining Graph Data is designed for a varied audience composed of professors, researchers and practitioners in industry. This volume is also suitable as a reference book for advanced-level database students in computer science and engineering.

Advances in technology are making massive data sets common in many scientific disciplines, such as astronomy, medical imaging, bio-informatics, combinatorial chemistry, remote sensing, and physics. To find useful information in these data sets, scientists and engineers are turning to data mining techniques. This book is a collection of papers based on the first two in a series of workshops on mining scientific datasets. It illustrates the diversity of problems and application areas that can benefit from data mining, as well as the issues and challenges that differentiate scientific data mining from its commercial counterpart. While the focus of the book is on mining scientific data, the work is of broader interest as many of the techniques can be applied equally well to data arising in business and web applications. Audience: This work would be an excellent text for students and researchers who are familiar with the basic principles of data mining and want to learn more about the application of data mining to their problem in science or engineering.

Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. - Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects - Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields - Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data

Data Mining and Data Visualization focuses on dealing with large-scale data, a field commonly referred to as data mining. The book is divided into three sections. The first deals with an introduction to statistical aspects of data mining and machine learning and includes applications to text analysis, computer intrusion detection, and hiding of information in digital files. The second section focuses on a variety of statistical methodologies that have proven to be effective in data mining applications. These include clustering, classification, multivariate density estimation, tree-based methods, pattern recognition, outlier detection, genetic algorithms, and dimensionality reduction. The third section focuses on data visualization and covers issues of visualization of high-dimensional data, novel graphical techniques with a focus on human factors, interactive graphics, and data visualization using virtual reality. This book represents a thorough cross section of internationally renowned thinkers who are inventing methods for dealing with a new data paradigm. - Distinguished contributors who are international experts in aspects of data mining - Includes data mining approaches to non-numerical data mining including text data, Internet traffic data, and geographic data - Highly topical discussions reflecting current thinking on contemporary technical issues, e.g. streaming data - Discusses taxonomy of dataset sizes, computational complexity, and scalability usually ignored in most discussions - Thorough discussion of data visualization issues blending statistical, human factors, and computational insights

The authors aim to shed light on the practicality of using machine learning in finding complex chemoinformatics and bioinformatics applications as well as identifiying AI in biological and chemical data points. The chapters are designed in such a way that they highlight the important role of AI in chemistry and bioinformatics particularly for the classification of diseases, selection of features and compounds, dimensionality reduction and more. In addition, they assist in the organization and optimal use of data points generated from experiments performed using AI techniques. This volume discusses the development of automated tools and techniques to aid in research plans. Features Covers AI applications in bioinformatics and chemoinformatics Demystifies the involvement of AI in generating biological and chemical data Provides an Introduction to basic and advanced chemoinformatics computational tools Presents a chemical biology based toolset for artificial intelligence usage in drug design Discusses computational methods in cancer, genome mapping, and stem cell research

Provides an extensive and up-to-date overview of the theory and application of computational pharmaceutics in the drug development process Exploring Computational Pharmaceutics - AI and Modeling in Pharma 4.0 introduces a variety of current and emerging computational techniques for pharmaceutical research. Bringing together experts from academia, industry, and regulatory agencies, this edited volume also explores the current state, key challenges, and future outlook of computational pharmaceutics while encouraging development across all sectors of the field. Throughout the text, the authors discuss a wide range of essential topics, from molecular modeling and process simulation to intelligent manufacturing and quantitative pharmacology. Building upon Exploring Computational Pharmaceutics - AI and Modeling in Pharma 4.0, this new edition provides a multi-scale perspective that reveals the physical, chemical, mathematical, and data-driven details of pre-formulation, formulation, process, and clinical studies, in addition to in vivo prediction in the human body and precision medicine in clinical settings. Detailed chapters address both conventional dosage forms and the application of computational technologies in advanced pharmaceutical research, such as dendrimer-based delivery systems, liposome and lipid membrane research, and inorganic nanoparticles. A major contribution to the development and promotion of computational pharmaceutics, this important resource: Discusses the development track, achievements, and prospects of computational pharmaceutics Presents multidisciplinary research to help physicists, chemists, mathematicians, and computer scientists locate problems in the field of drug delivery Covers a wide range of technologies, including complex formulations for water-insoluble drugs, protein/peptide formulations, nanomedicine, and gene delivery systems Focuses on the application of cutting-edge computational technologies and intelligent manufacturing of emerging pharmaceutical technologies Includes a systematic overview of computational pharmaceutics and Pharma 4.0 to assist non-specialist readers Covering introductory, advanced, and specialist topics, Exploring Computational Pharmaceutics - AI and Modeling in Pharma 4.0 is an invaluable resource for computational chemists, computational analysts, pharmaceutical chemists, process engineers, process managers, and pharmacologists, as well as computer scientists, medicinal chemists, clinical pharmacists, material scientists, and nanotechnology specialists working in the field.