Bioinformation Discovery

Bioinformation Discovery illustrates the power of biological data in knowledge discovery. It describes biological data types and representations with examples for creating a workflow in Bioinformation discovery. The concepts in knowledge discovery from data are illustrated using line diagrams. The principles and concepts in knowledge discovery are used for the development of prediction models for simulations of biological reactions and events. Advanced topics in molecular evolution and cellular & molecular biology are addressed using Bioinformation gleaned through discovery. Each chapter contains approximately 10 exercises for practice. This will help students to expand their problem solving skills in Bioinformation Discovery. Each chapter concludes with a number of good problem sets to test mastery of the material.

This new edition continues to illustrate the power of biological data in knowledge discovery. It describes biological data types and representations with examples for creating a workflow in bioinformation discovery. The concepts in knowledge discovery from data are illustrated using line diagrams. The principles and concepts in knowledge discovery are used for the development of prediction models for simulations of biological reactions and events. Advanced topics in molecular evolution and cellular & molecular biology are addressed using bioinformation gleaned through discovery. Each chapter contains approximately 10 exercises for practice. This will help students to expand their problem solving skills in Bioinformation Discovery. In this new edition, there are three new chapters covering single nucleotide polymorphism, genes, proteins and disease, and protein functions driven by surface electrostatics.

A comprehensive overview of the use of computational biology approaches in the drug discovery and development process.

The computational methods of bioinformatics are being used more and more to process the large volume of current biological data. Promoting an understanding of the underlying biology that produces this data, Pattern Discovery in Bioinformatics: Theory and Algorithms provides the tools to study regularities in biological data. Taking a systema

Demystifies Biomedical and Biological Big Data AnalysesBig Data Analysis for Bioinformatics and Biomedical Discoveries provides a practical guide to the nuts and bolts of Big Data, enabling you to quickly and effectively harness the power of Big Data to make groundbreaking biological discoveries, carry out translational medical research, and implem

The purpose of this edited book is to bring together the ideas and findings of data mining researchers and bioinformaticians by discussing cutting-edge research topics such as, gene expressions, protein/RNA structure prediction, phylogenetics, sequence and structural motifs, genomics and proteomics, gene findings, drug design, RNAi and microRNA analysis, text mining in bioinformatics, modelling of biochemical pathways, biomedical ontologies, system biology and pathways, and biological database management.

Everything you wanted to know about physics, biology, and the world at large becomes straightforward once you understand the life-organizing principle. Author E.M. Elsheikh, a mathematician and longtime professor, examines what the principle tells us about nature and life in this academic work. He proves that the secrets of our world reside in the quantum information contained in our DNA and genome. Discover the inner workings of life, and develop a better understanding of a maximum-action principle that explains self-organization, self-replication, and self-evolution. He also explores such topics as laws that describe phylogenetic evolution and ontogenetic development; little-known facts about genetics and evolution, including why Darwinian theory facilitates a more dynamic conception of human nature; extensions of quantum theory; and new foundations of knowledge. By challenging the notions of mainstream biology and physics and questioning assumptions about life being a physical rather than a supernatural phenomenon, youll stumble upon truths that few others know. Get ready to go on a fascinating journey that challenges paradigms and leads you to the Discovery of the Life-Organizing Principle.

This book is designed to introduce biologists, clinicians and computational researchers to fundamental data analysis principles, techniques and tools for supporting the discovery of biomarkers and the implementation of diagnostic/prognostic systems. The focus of the book is on how fundamental statistical and data mining approaches can support biomarker discovery and evaluation, emphasising applications based on different types of "omic" data. The book also discusses design factors, requirements and techniques for disease screening, diagnostic and prognostic applications. Readers are provided with the knowledge needed to assess the requirements, computational approaches and outputs in disease biomarker research. Commentaries from guest experts are also included, containing detailed discussions of methodologies and applications based on specific types of "omic" data, as well as their integration. Covers the main range of data sources currently used for biomarker discovery Covers the main range of data sources currently used for biomarker discovery Puts emphasis on concepts, design principles and methodologies that can be extended or tailored to more specific applications Offers principles and methods for assessing the bioinformatic/biostatistic limitations, strengths and challenges in biomarker discovery studies Discusses systems biology approaches and applications Includes expert chapter commentaries to further discuss relevance of techniques, summarize biological/clinical implications and provide alternative interpretations