Author : Anonim
Publisher : Unknown
Page : 347 pages
File Size : 51,5 Mb
Release : 2014
Category : Data mining
ISBN : 2527655998
Data Mining in Drug Discovery
Anonim
Data Mining In Drug Discovery
Data Mining In Drug Discovery Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Data Mining In Drug Discovery book. This book definitely worth reading, it is an incredibly well-written.
Pharmaceutical Data Mining
Konstantin V. Balakin
Author : Konstantin V. Balakin
Publisher : John Wiley & Sons
Page : 584 pages
File Size : 44,8 Mb
Release : 2009-11-19
Category : Medical
ISBN : 9780470567616
Pharmaceutical Data Mining by Konstantin V. Balakin Pdf
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.
Visual Data Mining
Mihael Ankerst,Georges Grinstein,Daniel A. Keim
Author : Mihael Ankerst,Georges Grinstein,Daniel A. Keim
Publisher : Unknown
Page : 128 pages
File Size : 50,8 Mb
Release : 2009
Category : Electronic
ISBN : OCLC:951145114
Visual Data Mining by Mihael Ankerst,Georges Grinstein,Daniel A. Keim Pdf
Data Mining in Drug Discovery
Rémy D. Hoffmann,Arnaud Gohier,Pavel Pospisil
Author : Rémy D. Hoffmann,Arnaud Gohier,Pavel Pospisil
Publisher : John Wiley & Sons
Page : 322 pages
File Size : 49,7 Mb
Release : 2013-09-25
Category : Medical
ISBN : 9783527656004
Data Mining in Drug Discovery by Rémy D. Hoffmann,Arnaud Gohier,Pavel Pospisil Pdf
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.
Data Mining in Medical and Biological Research
Eugenia Giannopoulou
Author : Eugenia Giannopoulou
Publisher : BoD – Books on Demand
Page : 334 pages
File Size : 49,6 Mb
Release : 2008-11-01
Category : Medical
ISBN : 9789537619305
Data Mining in Medical and Biological Research by Eugenia Giannopoulou Pdf
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.
Data Mining for Genomics and Proteomics
Darius M. Dziuda
Author : Darius M. Dziuda
Publisher : John Wiley & Sons
Page : 348 pages
File Size : 48,6 Mb
Release : 2010-07-16
Category : Computers
ISBN : 9780470593400
Data Mining for Genomics and Proteomics by Darius M. Dziuda Pdf
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.
Semantic Breakthrough in Drug Discovery
Bin Chen,Huijun Wang,Ying Ding,David Wild
Author : Bin Chen,Huijun Wang,Ying Ding,David Wild
Publisher : Springer Nature
Page : 10 pages
File Size : 55,7 Mb
Release : 2022-06-01
Category : Mathematics
ISBN : 9783031794568
Semantic Breakthrough in Drug Discovery by Bin Chen,Huijun Wang,Ying Ding,David Wild Pdf
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
Computational Medicinal Chemistry for Drug Discovery
Patrick Bultinck,Hans De Winter,Wilfried Langenaeker,Jan P. Tollenare
Author : Patrick Bultinck,Hans De Winter,Wilfried Langenaeker,Jan P. Tollenare
Publisher : CRC Press
Page : 844 pages
File Size : 48,9 Mb
Release : 2003-12-17
Category : Medical
ISBN : 0203913396
Computational Medicinal Chemistry for Drug Discovery by Patrick Bultinck,Hans De Winter,Wilfried Langenaeker,Jan P. Tollenare Pdf
Observing computational chemistry's proven value to the introduction of new medicines, Computational Medicinal Chemistry for Drug Discovery offers the techniques most frequently utilized by industry and academia for ligand design. Featuring contributions from more than 50 preeminent scientists, this book surveys molecular structure computation, intermolecular behavior, ligand-receptor interaction, and modeling. It also examines molecular mechanics, semi-empirical methods, wave function-based quantum chemistry, density functional theory, 3-D structure generation, and hybrid methods.
Biological Data Mining and Its Applications in Healthcare
Xiaoli Li,See-Kiong Ng,Jason T L Wang
Author : Xiaoli Li,See-Kiong Ng,Jason T L Wang
Publisher : World Scientific
Page : 436 pages
File Size : 53,6 Mb
Release : 2013-11-28
Category : Computers
ISBN : 9789814551021
Biological Data Mining and Its Applications in Healthcare by Xiaoli Li,See-Kiong Ng,Jason T L Wang Pdf
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
Data Mining in Structural Biology
I. Schlichting,U. Egner
Author : I. Schlichting,U. Egner
Publisher : Springer
Page : 209 pages
File Size : 44,6 Mb
Release : 2014-03-12
Category : Science
ISBN : 3662046466
Data Mining in Structural Biology by I. Schlichting,U. Egner Pdf
Structural biology is becoming a routine technique for structure de termination in pharmaceutical industries. The advances in molecular biology, crystal handling and data collection techniques, tunable syn chrotron radiation sources, and high-performance computing have all contributed to developments such as the production and expression of tailored protein domains, the use of the MAD (Multiple Anomalous Dispersion) method, and the collection of X-ray data from tiny crystals at cryogenic temperature. The number of protein structures deposited in the Protein Databank has increased tremendously over the last 3-4 years. Since 1997, more than 1,500 structures have been deposited each year, and during the first 7 months of this year, 1,500 protein structures were already deposited. The numerous initiatives in the field of "structural genomics" distributed all over the world have led to the development of techniques for high-throughput structure determina tion, thereby contributing to the increase in the determination of three dimensional protein structures. This structural information is being ex plored in various ways in the drug discovery process. It is not only used in structure-based drug design of new low-molecular-weight li gands, but also in the early stages of target validation and assessment. With the number of protein sequences without significant homology to well-known proteins increasing, the technique of structure-sequence compatibility (threading) is increasingly used to assign a function to a given protein fold.
Data Mining
Ian H. Witten,Eibe Frank
Author : Ian H. Witten,Eibe Frank
Publisher : Elsevier
Page : 558 pages
File Size : 42,5 Mb
Release : 2005-07-13
Category : Computers
ISBN : 9780080477022
Data Mining by Ian H. Witten,Eibe Frank Pdf
Data Mining, Second Edition, describes data mining techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights of this new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; and much more. This text is designed for information systems practitioners, programmers, consultants, developers, information technology managers, specification writers as well as professors and students of graduate-level data mining and machine learning courses. Algorithmic methods at the heart of successful data mining—including tried and true techniques as well as leading edge methods Performance improvement techniques that work by transforming the input or output
Data Mining: Know It All
Soumen Chakrabarti,Richard E. Neapolitan,Dorian Pyle,Mamdouh Refaat,Markus Schneider,Toby J. Teorey,Ian H. Witten,Earl Cox,Eibe Frank,Ralf Hartmut Güting,Jiawei Han,Xia Jiang,Micheline Kamber,Sam S. Lightstone,Thomas P. Nadeau
Author : Soumen Chakrabarti,Richard E. Neapolitan,Dorian Pyle,Mamdouh Refaat,Markus Schneider,Toby J. Teorey,Ian H. Witten,Earl Cox,Eibe Frank,Ralf Hartmut Güting,Jiawei Han,Xia Jiang,Micheline Kamber,Sam S. Lightstone,Thomas P. Nadeau
Publisher : Morgan Kaufmann
Page : 477 pages
File Size : 52,6 Mb
Release : 2008-10-31
Category : Computers
ISBN : 9780080877884
Data Mining: Know It All by Soumen Chakrabarti,Richard E. Neapolitan,Dorian Pyle,Mamdouh Refaat,Markus Schneider,Toby J. Teorey,Ian H. Witten,Earl Cox,Eibe Frank,Ralf Hartmut Güting,Jiawei Han,Xia Jiang,Micheline Kamber,Sam S. Lightstone,Thomas P. Nadeau Pdf
This book brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases. It consolidates both introductory and advanced topics, thereby covering the gamut of data mining and machine learning tactics ? from data integration and pre-processing, to fundamental algorithms, to optimization techniques and web mining methodology. The proposed book expertly combines the finest data mining material from the Morgan Kaufmann portfolio. Individual chapters are derived from a select group of MK books authored by the best and brightest in the field. These chapters are combined into one comprehensive volume in a way that allows it to be used as a reference work for those interested in new and developing aspects of data mining. This book represents a quick and efficient way to unite valuable content from leading data mining experts, thereby creating a definitive, one-stop-shopping opportunity for customers to receive the information they would otherwise need to round up from separate sources. Chapters contributed by various recognized experts in the field let the reader remain up to date and fully informed from multiple viewpoints. Presents multiple methods of analysis and algorithmic problem-solving techniques, enhancing the reader’s technical expertise and ability to implement practical solutions. Coverage of both theory and practice brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases.
Artificial Intelligence in Data Mining
D. Binu,B.R. Rajakumar
Author : D. Binu,B.R. Rajakumar
Publisher : Academic Press
Page : 270 pages
File Size : 40,7 Mb
Release : 2021-02-17
Category : Science
ISBN : 9780128206164
Artificial Intelligence in Data Mining by D. Binu,B.R. Rajakumar Pdf
Artificial Intelligence in Data Mining: Theories and Applications offers a comprehensive introduction to data mining theories, relevant AI techniques, and their many real-world applications. This book is written by experienced engineers for engineers, biomedical engineers, and researchers in neural networks, as well as computer scientists with an interest in the area. Provides coverage of the fundamentals of Artificial Intelligence as applied to data mining, including computational intelligence and unsupervised learning methods for data clustering Presents coverage of key topics such as heuristic methods for data clustering, deep learning methods for data classification, and neural networks Includes case studies and real-world applications of AI techniques in data mining, for improved outcomes in clinical diagnosis, satellite data extraction, agriculture, security and defense
Interactive Knowledge Discovery and Data Mining in Biomedical Informatics
Andreas Holzinger,Igor Jurisica
Author : Andreas Holzinger,Igor Jurisica
Publisher : Springer
Page : 357 pages
File Size : 49,8 Mb
Release : 2014-06-17
Category : Computers
ISBN : 9783662439685
Interactive Knowledge Discovery and Data Mining in Biomedical Informatics by Andreas Holzinger,Igor Jurisica Pdf
One of the grand challenges in our digital world are the large, complex and often weakly structured data sets, and massive amounts of unstructured information. This “big data” challenge is most evident in biomedical informatics: the trend towards precision medicine has resulted in an explosion in the amount of generated biomedical data sets. Despite the fact that human experts are very good at pattern recognition in dimensions of = 3; most of the data is high-dimensional, which makes manual analysis often impossible and neither the medical doctor nor the biomedical researcher can memorize all these facts. A synergistic combination of methodologies and approaches of two fields offer ideal conditions towards unraveling these problems: Human–Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the goal of supporting human capabilities with machine learning./ppThis state-of-the-art survey is an output of the HCI-KDD expert network and features 19 carefully selected and reviewed papers related to seven hot and promising research areas: Area 1: Data Integration, Data Pre-processing and Data Mapping; Area 2: Data Mining Algorithms; Area 3: Graph-based Data Mining; Area 4: Entropy-Based Data Mining; Area 5: Topological Data Mining; Area 6 Data Visualization and Area 7: Privacy, Data Protection, Safety and Security.
Innovations and Implementations of Computer Aided Drug Discovery Strategies in Rational Drug Design
Sanjeev Kumar Singh
Author : Sanjeev Kumar Singh
Publisher : Springer Nature
Page : 334 pages
File Size : 40,8 Mb
Release : 2021-02-02
Category : Science
ISBN : 9789811589362
Innovations and Implementations of Computer Aided Drug Discovery Strategies in Rational Drug Design by Sanjeev Kumar Singh Pdf
This book presents various computer-aided drug discovery methods for the design and development of ligand and structure-based drug molecules. A wide variety of computational approaches are now being used in various stages of drug discovery and development, as well as in clinical studies. Yet, despite the rapid advances in computer software and hardware, combined with the exponential growth in the available biological information, there are many challenges that still need to be addressed, as this book shows. In turn, it shares valuable insights into receptor-ligand interactions in connection with various biological functions and human diseases. The book discusses a wide range of phylogenetic methods and highlights the applications of Molecular Dynamics Simulation in the drug discovery process. It also explores the application of quantum mechanics in order to provide better accuracy when calculating protein-ligand binding interactions and predicting binding affinities. In closing, the book provides illustrative descriptions of major challenges associated with computer-aided drug discovery for the development of therapeutic drugs. Given its scope, it offers a valuable asset for life sciences researchers, medicinal chemists and bioinformaticians looking for the latest information on computer-aided methodologies for drug development, together with their applications in drug discovery.
