Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology discusses the latest developments in all aspects of computational biology, bioinformatics, and systems biology and the application of data-analytics and algorithms, mathematical modeling, and simu- lation techniques. • Discusses the development and application of data-analytical and theoretical methods, mathematical modeling, and computational simulation techniques to the study of biological and behavioral systems, including applications in cancer research, computational intelligence and drug design, high-performance computing, and biology, as well as cloud and grid computing for the storage and access of big data sets. • Presents a systematic approach for storing, retrieving, organizing, and analyzing biological data using software tools with applications to general principles of DNA/RNA structure, bioinformatics and applications, genomes, protein structure, and modeling and classification, as well as microarray analysis. • Provides a systems biology perspective, including general guidelines and techniques for obtaining, integrating, and analyzing complex data sets from multiple experimental sources using computational tools and software. Topics covered include phenomics, genomics, epigenomics/epigenetics, metabolomics, cell cycle and checkpoint control, and systems biology and vaccination research. • Explains how to effectively harness the power of Big Data tools when data sets are so large and complex that it is difficult to process them using conventional database management systems or traditional data processing applications. - Discusses the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological and behavioral systems. - Presents a systematic approach for storing, retrieving, organizing and analyzing biological data using software tools with applications. - Provides a systems biology perspective including general guidelines and techniques for obtaining, integrating and analyzing complex data sets from multiple experimental sources using computational tools and software.

Emerging Trends in Applications and Infrastructures for Computational Biology, Bioinformatics, and Systems Biology: Systems and Applications covers the latest trends in the field with special emphasis on their applications. The first part covers the major areas of computational biology, development and application of data-analytical and theoretical methods, mathematical modeling, and computational simulation techniques for the study of biological and behavioral systems. The second part covers bioinformatics, an interdisciplinary field concerned with methods for storing, retrieving, organizing, and analyzing biological data. The book also explores the software tools used to generate useful biological knowledge. The third part, on systems biology, explores how to obtain, integrate, and analyze complex datasets from multiple experimental sources using interdisciplinary tools and techniques, with the final section focusing on big data and the collection of datasets so large and complex that it becomes difficult to process using conventional database management systems or traditional data processing applications. Explores all the latest advances in this fast-developing field from an applied perspective Provides the only coherent and comprehensive treatment of the subject available Covers the algorithm development, software design, and database applications that have been developed to foster research

The last decade has witnessed various technological advances in life sciences, especially high throughput technologies. These technologies provide a way to perform parallel scientific studies in a very short period of time with low cost. High throughput techniques, mainly, next generation sequencing, microarray and mass spectrometry, have strengthened the omics vision in the last decades (study of complete system) and now resulted in well-developed branches of omics i.e., genomics, transcriptomics, proteomics and metabolomics, which deal with almost every level of central dogma of life. Practice of high throughput techniques throughout the world with different aims and objectives resulted in a voluminous data, which required computational applications, i.e., database, algorithm and software to store, process and get biological interpretation from primary raw data. Researchers from different fields are looking to analyze these raw data for different purposes, but lacking of proper information and knowledge in proper documented form creates different kinds of hurdles and raises the challenges. This book contains thirteen chapters that deal with different computational biology/bioinformatics resources and concepts which are already in practice by the scientific community or can be utilized to handle various aspects of different classes of omics data. It includes different computational concepts, algorithm, resources and recent trends belonging to the four major branches of omics (i.e., genomics, transcriptomics, proteomics and metabolomics), including integrative omics. It will help all scholars who are working in any branch of computational omics and bioinformatics field as well as those who would like to perform research at a systemic biology through computational approaches.

A detailed overview of current research in kernel methods and their application to computational biology.

The advances in biotechnology such as the next generation sequencing technologies are occurring at breathtaking speed. Advances and breakthroughs give competitive advantages to those who are prepared. However, the driving force behind the positive competition is not only limited to the technological advancement, but also to the companion data analy

Systems Biology Modelling and Analysis Describes important modelling and computational methods for systems biology research to enable practitioners to select and use the most suitable technique Systems Biology Modelling and Analysis provides an overview of state-of-the-art techniques and introduces related tools and practices to formalize models and automate reasoning for systems biology. The authors present and compare the main formal methods used in systems biology for modelling biological networks, including discussion of their advantages, drawbacks, and main applications. Each chapter includes an intuitive presentation of the specific formalism, a brief history of the formalism and of its applications in systems biology, a formal description of the formalism and its variants, at least one realistic case study, some applications of formal techniques to validate and make deep analysis of models encoded with the formalism, and a discussion on the kind of biological systems for which the formalism is suited, along with concrete ideas on its possible evolution. Edited by a highly qualified expert with significant experience in the field, some of the methods and techniques covered in Systems Biology Modelling and Analysis include: Petri nets, an important tool for studying different aspects of biological systems, ranging from simple signaling pathways to metabolic networks and beyond Pathway Logic, a formal, rule-based system and interactive viewer for developing executable models of cellular processes Boolean networks, a mathematical model which has been widely used for decades in the context of biological regulation networks Answer Set Programming (ASP), which has proven to be a strong logic programming paradigm to deal with the inherent complexity of biological models For systems biologists, biochemists, bioinformaticians, molecular biologists, pharmacologists, and computer scientists, Systems Biology Modelling and Analysis is a comprehensive all-in-one resource to understand and harness the field’s current models and techniques while also preparing for their potential developments in coming years with the help of the author’s expert insight.

Alternative techniques and tools for analyzing biomolecular networks With the recent rapid advances in molecular biology, high-throughput experimental methods have resulted in enormous amounts of data that can be used to study biomolecular networks in living organisms. With this development has come recognition of the fact that a complicated living organism cannot be fully understood by merely analyzing individual components. Rather, it is the interactions of components or biomolecular networks that are ultimately responsible for an organism's form and function. This book addresses the important need for a new set of computational tools to reveal essential biological mechanisms from a systems biology approach. Readers will get comprehensive coverage of analyzing biomolecular networks in cellular systems based on available experimental data with an emphasis on the aspects of network, system, integration, and engineering. Each topic is treated in depth with specific biological problems and novel computational methods: GENE NETWORKS—Transcriptional regulation; reconstruction of gene regulatory networks; and inference of transcriptional regulatory networks PROTEIN INTERACTION NETWORKS—Prediction of protein-protein interactions; topological structure of biomolecular networks; alignment of biomolecular networks; and network-based prediction of protein function METABOLIC NETWORKS AND SIGNALING NETWORKS—Analysis, reconstruction, and applications of metabolic networks; modeling and inference of signaling networks; and other topics and new trends In addition to theoretical results and methods, many computational software tools are referenced and available from the authors' Web sites. Biomolecular Networks is an indispensable reference for researchers and graduate students in bioinformatics, computational biology, systems biology, computer science, and applied mathematics.

Over one hundred presentations from the 36th annual Charleston Library Conference (held November 1-5, 2016) are included in this annual proceedings volume. Major themes of the meeting included data visualization, streaming video, analysis and assessment, demand-driven acquisition, and open access publishing. While the Charleston meeting remains a core one for acquisitions librarians in dialog with publishers and vendors, the breadth of coverage of this volume reflects the fact that this conference is now one of the major venues for leaders in the publishing and library communities to shape strategy and prepare for the future. Almost 2,000 delegates attended the 2016 meeting, ranging from the staff of small public library systems to the CEOs of major corporations. This fully indexed, copyedited volume provides a rich source for the latest evidence-based research and lessons from practice in a range of information science fields. Contributors comprise leaders in the library, publishing, and vendor communities.

Molecular Docking for Computer-Aided Drug Design: Fundamentals, Techniques, Resources and Applications offers in-depth coverage on the use of molecular docking for drug design. The book is divided into three main sections that cover basic techniques, tools, web servers and applications. It is an essential reference for students and researchers involved in drug design and discovery. - Covers the latest information and state-of-the-art trends in structure-based drug design methodologies - Includes case studies that complement learning - Consolidates fundamental concepts and current practice of molecular docking into one convenient resource

Despite major advances in healthcare over the past century, the successful treatment of cancer has remained a significant challenge, and cancers are the second leading cause of death worldwide behind cardiovascular disease. Early detection and survival are important issues to control cancer. The development of quantitative methods and computer technology has facilitated the formation of new models in medical and biological sciences. The application of mathematical modelling in solving many real-world problems in medicine and biology has yielded fruitful results. In spite of advancements in instrumentations technology and biomedical equipment, it is not always possible to perform experiments in medicine and biology for various reasons. Thus, mathematical modelling and simulation are viewed as viable alternatives in such situations, and are discussed in this book. The conventional diagnostic techniques of cancer are not always effective as they rely on the physical and morphological appearance of the tumour. Early stage prediction and diagnosis is very difficult with conventional techniques. It is well known that cancers are involved in genome level changes. As of now, the prognosis of various types of cancer depends upon findings related to the data generated through different experiments. Several machine learning techniques exist in analysing the data of expressed genes; however, the recent results related with deep learning algorithms are more accurate and accommodative, as they are effective in selecting and classifying informative genes. This book explores the probabilistic computational deep learning model for cancer classification and prediction.