Computational And Analytic Methods In Biological Sciences

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.

This contributed volume collects papers presented at a special session of the conference Computational and Mathematical Methods in Science and Engineering (CMMSE) held in Cadiz, Spain from June 30 - July 6, 2019. Covering the applications of integral methods to scientific developments in a variety of fields, ranging from pure analysis to petroleum engineering, the chapters in this volume present new results in both pure and applied mathematics. Written by well-known researchers in their respective disciplines, each chapter shares a common methodology based on a combination of analytic and computational tools. This approach makes the collection a valuable, multidisciplinary reference on how mathematics can be applied to various real-world processes and phenomena. Computational and Analytic Methods in Science and Engineering will be ideal for applied mathematicians, physicists, and research engineers.

This volume details computational techniques for analyses of a wide range of biological contexts, providing an overview of the most up-to-date techniques used in the field. Chapters guide the reader through available data resources and analysis methods and easy-to-follow protocols that allow the researcher to apply various computational tools to an array of different data types. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory and computational protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Computational Cell Biology: Method and Protocols aims to ensure successful results in the further study of this vital field.

Computational biology refers to the study of behavioral, biological, social and ecological systems through the implementation of theoretical and data-analytical methods, computational simulation approaches, and mathematical modeling. It involves the study of biological data in order to create models or algorithms, which can be used to better understand biological systems and relationships. It also assists in the sequencing of the human genome, modeling of biological systems, constructing accurate models of the human brain, and mapping the 3D structure of genomes. There are numerous methods which are used within this field such as unsupervised learning, supervised learning and graph analytics. Unsupervised learning refers to a type of algorithm which is used to find patterns in unlabeled data. This book provides comprehensive insights into the field of computational biology. It presents researches and studies performed by experts across the globe. This book aims to serve as a resource guide for students and experts alike and contribute to the growth of computational biology.

An invaluable tool in Bioinformatics, this unique volume provides both theoretical and experimental results, and describes basic principles of computational intelligence and pattern analysis while deepening the reader's understanding of the ways in which these principles can be used for analyzing biological data in an efficient manner. This book synthesizes current research in the integration of computational intelligence and pattern analysis techniques, either individually or in a hybridized manner. The purpose is to analyze biological data and enable extraction of more meaningful information and insight from it. Biological data for analysis include sequence data, secondary and tertiary structure data, and microarray data. These data types are complex and advanced methods are required, including the use of domain-specific knowledge for reducing search space, dealing with uncertainty, partial truth and imprecision, efficient linear and/or sub-linear scalability, incremental approaches to knowledge discovery, and increased level and intelligence of interactivity with human experts and decision makers Chapters authored by leading researchers in CI in biology informatics. Covers highly relevant topics: rational drug design; analysis of microRNAs and their involvement in human diseases. Supplementary material included: program code and relevant data sets correspond to chapters.

Analytical Techniques in Biosciences: From Basics to Applications presents comprehensive and up-to-date information on the various analytical techniques obtainable in bioscience research laboratories across the world. This book contains chapters that discuss the basic bioanalytical protocols and sample preparation guidelines. Commonly encountered analytical techniques, their working principles, and applications were presented. Techniques, considered in this book, include centrifugation techniques, electrophoretic techniques, chromatography, titrimetry, spectrometry, and hyphenated techniques. Subsequent chapters emphasize molecular weight determination and electroanalytical techniques, biosensors, and enzyme assay protocols. Other chapters detail microbial techniques, statistical methods, computational modeling, and immunology and immunochemistry. The book draws from experts from key institutions around the globe, who have simplified the chapters in a way that will be useful to early-stage researchers as well as advanced scientists. It is also carefully structured and integrated sequentially to aid flow, consistency, and continuity. This is a must-have reference for graduate students and researchers in the field of biosciences. Presents basic analytical protocols and sample-preparation guidelines Details the various analytical techniques, including centrifugation, spectrometry, chromatography, and titrimetry Describes advanced techniques such as hyphenated techniques, electroanalytical techniques, and the application of biosensors in biomedical research Presents biostatistical tools and methods and basic computational models in biosciences

Analytical methods are the essential enabling tools of the modern biosciences. This book presents a comprehensive introduction into these analytical methods, including their physical and chemical backgrounds, as well as a discussion of the strengths and weakness of each method. It covers all major techniques for the determination and experimental analysis of biological macromolecules, including proteins, carbohydrates, lipids and nucleic acids. The presentation includes frequent cross-references in order to highlight the many connections between different techniques. The book provides a bird's eye view of the entire subject and enables the reader to select the most appropriate method for any given bioanalytical challenge. This makes the book a handy resource for students and researchers in setting up and evaluating experimental research. The depth of the analysis and the comprehensive nature of the coverage mean that there is also a great deal of new material, even for experienced experimentalists. The following techniques are covered in detail: - Purification and determination of proteins - Measuring enzymatic activity - Microcalorimetry - Immunoassays, affinity chromatography and other immunological methods - Cross-linking, cleavage, and chemical modification of proteins - Light microscopy, electron microscopy and atomic force microscopy - Chromatographic and electrophoretic techniques - Protein sequence and composition analysis - Mass spectrometry methods - Measuring protein-protein interactions - Biosensors - NMR and EPR of biomolecules - Electron microscopy and X-ray structure analysis - Carbohydrate and lipid analysis - Analysis of posttranslational modifications - Isolation and determination of nucleic acids - DNA hybridization techniques - Polymerase chain reaction techniques - Protein sequence and composition analysis - DNA sequence and epigenetic modification analysis - Analysis of protein-nucleic acid interactions - Analysis of sequence data - Proteomics, metabolomics, peptidomics and toponomics - Chemical biology

This 2006 guide to the contemporary toolbox of methods for data analysis will serve graduate students and researchers across the biological sciences. Modern computational tools, such as Maximum Likelihood, Monte Carlo and Bayesian methods, mean that data analysis no longer depends on elaborate assumptions designed to make analytical approaches tractable. These new 'computer-intensive' methods are currently not consistently available in statistical software packages and often require more detailed instructions. The purpose of this book therefore is to introduce some of the most common of these methods by providing a relatively simple description of the techniques. Examples of their application are provided throughout, using real data taken from a wide range of biological research. A series of software instructions for the statistical software package S-PLUS are provided along with problems and solutions for each chapter.