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Clustering in Bioinformatics and Drug Discovery by John David MacCuish,Norah E. MacCuish Pdf
With a DVD of color figures, Clustering in Bioinformatics and Drug Discovery provides an expert guide on extracting the most pertinent information from pharmaceutical and biomedical data. It offers a concise overview of common and recent clustering methods used in bioinformatics and drug discovery.Setting the stage for subsequent material, the firs
Clustering in Bioinformatics and Drug Discovery by JOHN DAVID. MACCUISH MACCUISH (NORAH E.),Norah E Maccuish Pdf
With a DVD of color figures, Clustering in Bioinformatics and Drug Discovery provides an expert guide on extracting the most pertinent information from pharmaceutical and biomedical data. It offers a concise overview of common and recent clustering methods used in bioinformatics and drug discovery. Setting the stage for subsequent material, the first three chapters of the book introduce statistical learning theory, exploratory data analysis, clustering algorithms, different types of data, graph theory, and various clustering forms. In the following chapters on partitional, cluster sampling, and hierarchical algorithms, the book provides readers with enough detail to obtain a basic understanding of cluster analysis for bioinformatics and drug discovery. The remaining chapters cover more advanced methods, such as hybrid and parallel algorithms, as well as details related to specific types of data, including asymmetry, ambiguity, validation measures, and visualization. This book explores the application of cluster analysis in the areas of bioinformatics and cheminformatics as they relate to drug discovery. Clarifying the use and misuse of clustering methods, it helps readers understand the relative merits of these methods and evaluate results so that useful hypotheses can be developed and tested.
Integrative Cluster Analysis in Bioinformatics by Basel Abu-Jamous,Rui Fa,Asoke K. Nandi Pdf
Clustering techniques are increasingly being put to use in the analysis of high-throughput biological datasets. Novel computational techniques to analyse high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. This book details the complete pathway of cluster analysis, from the basics of molecular biology to the generation of biological knowledge. The book also presents the latest clustering methods and clustering validation, thereby offering the reader a comprehensive review of clustering analysis in bioinformatics from the fundamentals through to state-of-the-art techniques and applications. Key Features: Offers a contemporary review of clustering methods and applications in the field of bioinformatics, with particular emphasis on gene expression analysis Provides an excellent introduction to molecular biology with computer scientists and information engineering researchers in mind, laying out the basic biological knowledge behind the application of clustering analysis techniques in bioinformatics Explains the structure and properties of many types of high-throughput datasets commonly found in biological studies Discusses how clustering methods and their possible successors would be used to enhance the pace of biological discoveries in the future Includes a companion website hosting a selected collection of codes and links to publicly available datasets
Data Clustering: Theory, Algorithms, and Applications, Second Edition by Guojun Gan,Chaoqun Ma,Jianhong Wu Pdf
Data clustering, also known as cluster analysis, is an unsupervised process that divides a set of objects into homogeneous groups. Since the publication of the first edition of this monograph in 2007, development in the area has exploded, especially in clustering algorithms for big data and open-source software for cluster analysis. This second edition reflects these new developments, covers the basics of data clustering, includes a list of popular clustering algorithms, and provides program code that helps users implement clustering algorithms. Data Clustering: Theory, Algorithms and Applications, Second Edition will be of interest to researchers, practitioners, and data scientists as well as undergraduate and graduate students.
Anti-Obesity Drug Discovery and Development by Atta-ur- Rahman,M. Iqbal Choudhary Pdf
"Obesity is a complex health problem, caused by a number of factors such as excessive food intake, lack of physical activity, genetic predisposition, endocrine disorders, medications and psychiatric illnesses. Onset of obesity in both the developing and th"
Bioinformatics Research and Development by Sepp Hochreiter Pdf
This book constitutes the refereed proceedings of the First International Bioinformatics Research and Development Conference, BIRD 2007, held in Berlin, Germany in March 2007. The 36 revised full papers are organized in topical sections on microarray and systems biology and networks, medical, SNPs, genomics, systems biology, sequence analysis and coding, proteomics and structure, databases, Web and text analysis.
Applied Biclustering Methods for Big and High-Dimensional Data Using R by Adetayo Kasim,Ziv Shkedy,Sebastian Kaiser,Sepp Hochreiter,Willem Talloen Pdf
Proven Methods for Big Data Analysis As big data has become standard in many application areas, challenges have arisen related to methodology and software development, including how to discover meaningful patterns in the vast amounts of data. Addressing these problems, Applied Biclustering Methods for Big and High-Dimensional Data Using R shows how to apply biclustering methods to find local patterns in a big data matrix. The book presents an overview of data analysis using biclustering methods from a practical point of view. Real case studies in drug discovery, genetics, marketing research, biology, toxicity, and sports illustrate the use of several biclustering methods. References to technical details of the methods are provided for readers who wish to investigate the full theoretical background. All the methods are accompanied with R examples that show how to conduct the analyses. The examples, software, and other materials are available on a supplementary website.
Similarity-Based Clustering by Thomas Villmann,M. Biehl,Barbara Hammer,Michel Verleysen Pdf
This book is the outcome of the Dagstuhl Seminar on "Similarity-Based Clustering" held at Dagstuhl Castle, Germany, in Spring 2007. In three chapters, the three fundamental aspects of a theoretical background, the representation of data and their connection to algorithms, and particular challenging applications are considered. Topics discussed concern a theoretical investigation and foundation of prototype based learning algorithms, the development and extension of models to directions such as general data structures and the application for the domain of medicine and biology. Similarity based methods find widespread applications in diverse application domains, including biomedical problems, but also in remote sensing, geoscience or other technical domains. The presentations give a good overview about important research results in similarity-based learning, whereby the character of overview articles with references to correlated research articles makes the contributions particularly suited for a first reading concerning these topics.
Data Analytics in Bioinformatics by Rabinarayan Satpathy,Tanupriya Choudhury,Suneeta Satpathy,Sachi Nandan Mohanty,Xiaobo Zhang Pdf
Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more.
Frontiers in Anti-Infective Drug Discovery: Volume 8 by Atta-ur-Rahman,Muhammad Iqbal Choudhary Pdf
This book series brings updated reviews to readers interested in advances in the development of anti-infective drug design and discovery. The scope of the book series covers a range of topics including rational drug design and drug discovery, medicinal chemistry, in-silico drug design, combinatorial chemistry, high-throughput screening, drug targets, recent important patents, and structure-activity relationships. Frontiers in Anti-Infective Drug Discovery is a valuable resource for pharmaceutical scientists and post-graduate students seeking updated and critically important information for developing clinical trials and devising research plans in this field. The eighth volume of this series features 8 chapters that cover methods for antimicrobial drug discovery (with 2 chapters that focus on genomics) as well as updates on drug development against Helicobacter pylori and emerging coronaviruses, among other interesting topics: - Eradication of Helicobacter pylori Infection with Non-Bismuth Quadruple Concomitant Therapy - Drug Discovery Strategies Against Emerging Coronaviruses: A Global Threat - Opportunities Offered By Fragment-Based Drug Design in Antibiotic Development - Phage therapy as a Tool for Control of Foodborne Diseases: Advantages and Limitations - Subtractive Genomics Approaches: Towards Anti-Bacterial Drug Discovery - Recent Advances in the Discovery of Antimicrobials through Metagenomics - Phyto-Nano-Antimicrobials: Synthesis, Characterization, Discovery, and Advances - Aptamers as Anti-infective Agents.
Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare by Mark Chang Pdf
Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare covers exciting developments at the intersection of computer science and statistics. While much of machine-learning is statistics-based, achievements in deep learning for image and language processing rely on computer science’s use of big data. Aimed at those with a statistical background who want to use their strengths in pursuing AI research, the book: · Covers broad AI topics in drug development, precision medicine, and healthcare. · Elaborates on supervised, unsupervised, reinforcement, and evolutionary learning methods. · Introduces the similarity principle and related AI methods for both big and small data problems. · Offers a balance of statistical and algorithm-based approaches to AI. · Provides examples and real-world applications with hands-on R code. · Suggests the path forward for AI in medicine and artificial general intelligence. As well as covering the history of AI and the innovative ideas, methodologies and software implementation of the field, the book offers a comprehensive review of AI applications in medical sciences. In addition, readers will benefit from hands on exercises, with included R code.
Industrial Clusters in Biotechnology by Chiesa Vittorio Chiaroni Davide,Vittorio Chiesa,Davide Chiaroni Pdf
Annotation - the preconditions for a cluster to grow (scientific base and/or industrial base, innovative financing, etc.); - the driving forces for cluster growth and development, i.e. the key factors of development (new company creation, IP rules, acceptance of biotech products, services and infrastructures, etc.); - best practices in cluster management (barrier removal, network creation, marketing, technology transfer, etc.).
RNA-Seq in Drug Discovery and Development by Feng Cheng,Robert Morris Pdf
The research and development process in modern drug discovery and development is a complex and challenging task. Using traditional biological test methods such as PCR to measure the expression levels or function of these genes is costly and time-consuming. RNA-seq can measure the expression patterns of thousands of genes simultaneously and provide insights into functional pathways or regulations in biological processes, which has revolutionized the way biological scientists examine gene functions. This book addresses the various aspects of the RNA-seq technique, especially its application in drug discovery and development. Features • One of the few books that focuses on the applications of the RNA-seq technique in drug discovery and development. • Comprehensive and timely publication which relates RNA sequencing to drug targets, mechanisms of action, and resistance. • The editor has extensive experience in the field of computational medicinal chemistry, computational biophysics, and bioinformatics. • Chapter authors are at the frontline of the academic and industrial science in this particular area of RNA sequencing.
