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.

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.

The exponentially increasing amounts of data associated with drug discovery being generated each year make getting useful information from that data more and more critical. With a central repository to keep the massive amounts of data, organizations need tools that can help them extract the most useful information from the data. A data warehouse can bring together data in a single format, supplemented by metadata through the use of a set of input mechanisms known as extraction, transformation, and loading (ETL) tools. Extraction of the data can be either extracting existing data or the data that is imported to the database, transformation is when the data is translated to the format the database can understand. Transformation makes the new format of the data consistent with the other existing data. Finally, the formatted data can be loaded into files and the link address of the data is saved in tables in the database for further analysis. Analysis of the data includes simple query and reporting, statistical analysis, complex multidimensional analysis, and data mining. Large quantities of data are searched and analyzed to discover useful patterns or relationships, which are then used to predict behavior. The purpose of this project is to produce a repository database of drugs, drug features (properties), and drug targets where data can be mined and analyzed. Drug targets are different proteins that drugs try to bind to stop the activities of the protein. For example, g-secretase is a protein that causes Alzheimer's. There are certain drugs that can bind to g-secretase to stop its functionality which in turn may stop Alzheimer's disease. Users can utilize the database to mine useful data to predict the specific chemical properties that will have the relative efficacy of a specific target and the coefficient for each chemical property. This database can be equipped with different data mining approaches/algorithms such as linear, non-linear, and classification types of data modeling. The data models have enhanced with the Genetic Evolution (GE) algorithms [1, 2, through 17]. This paper discusses implementation with the linear data models such as Multiple Linear Regression (MLR) [18], Partial Least Square Regression (PLSR) [19], and Support Vector Machine (SVM) [20].

Data Mining for Genomics and Proteomics uses pragmatic examples and a complete case study to demonstrate step-by-step how biomedical studies can be used to maximize the chance of extracting new and useful biomedical knowledge from data. It is an excellent resource for students and professionals involved with gene or protein expression data in a variety of settings.

The book covers current developments in the field of expert applications and security, which employ advances of next-generation communication and computational technology to shape real-world applications. It gathers selected research papers presented at the ICETEAS 2018 conference, which was held at Jaipur Engineering College and Research Centre, Jaipur, India, on February 17–18, 2018. Key topics covered include expert applications and artificial intelligence; information and application security; advanced computing; multimedia applications in forensics, security and intelligence; and advances in web technologies: implementation and security issues.

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The current drug development paradigm---sometimes expressed as, ``One disease, one target, one drug''---is under question, as relatively few drugs have reached the market in the last two decades. Meanwhile, the research focus of drug discovery is being placed on the study of drug action on biological systems as a whole, rather than on individual components of such systems. The vast amount of biological information about genes and proteins and their modulation by small molecules is pushing drug discovery to its next critical steps, involving the integration of chemical knowledge with these biological databases. Systematic integration of these heterogeneous datasets and the provision of algorithms to mine the integrated datasets would enable investigation of the complex mechanisms of drug action; however, traditional approaches face challenges in the representation and integration of multi-scale datasets, and in the discovery of underlying knowledge in the integrated datasets. The Semantic Web, envisioned to enable machines to understand and respond to complex human requests and to retrieve relevant, yet distributed, data, has the potential to trigger system-level chemical-biological innovations. Chem2Bio2RDF is presented as an example of utilizing Semantic Web technologies to enable intelligent analyses for drug discovery.Table of Contents: Introduction / Data Representation and Integration Using RDF / Data Representation and Integration Using OWL / Finding Complex Biological Relationships in PubMed Articles using Bio-LDA / Integrated Semantic Approach for Systems Chemical Biology Knowledge Discovery / Semantic Link Association Prediction / Conclusions / References / Authors' Biographies