Advanced Biochemistry Tools Techniques And Applications
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Advanced Biochemistry: Tools, Techniques, and Applications
Author: Mr. Tarun Sharma
language: en
Publisher: Academic Guru Publishing House
Release Date: 2025-02-24
Advanced Biochemistry: Tools, Techniques, and Applica-tions" is a specialized textbook designed to provide a detailed exploration of the tools and techniques fundamental to modern biochemistry. It offers a thorough understanding of biochemical processes at molecular, cellular, and systemic levels, supported by insights into the most advanced laboratory techniques used in biochemical research today. The book is organized into structured chapters, each focusing on a key aspect of biochemistry. Topics range from enzyme kinetics, protein structure, and metabolic pathways to cutting-edge techniques like PCR, mass spectrometry, and CRISPR gene editing. Each chapter not only explains the theoretical background but also delves into practical applications in drug discovery, personalized medicine, biotechnology, and clinical diagnostics. Designed for a wide audience, the book is an indispensable resource for students pursuing degrees in biochemistry and related fields. It is also a useful reference for research-ers and professionals looking to update their knowledge of emerging technologies and their practical implications in biochemistry. The book emphasizes the importance of hands-on experience with analytical techniques and the integration of advanced tools in solving complex biochem-ical problems, making it a crucial resource for both academic learning and professional practice.
Advanced AI Techniques and Applications in Bioinformatics
The advanced AI techniques are essential for resolving various problematic aspects emerging in the field of bioinformatics. This book covers the recent approaches in artificial intelligence and machine learning methods and their applications in Genome and Gene editing, cancer drug discovery classification, and the protein folding algorithms among others. Deep learning, which is widely used in image processing, is also applicable in bioinformatics as one of the most popular artificial intelligence approaches. The wide range of applications discussed in this book are an indispensable resource for computer scientists, engineers, biologists, mathematicians, physicians, and medical informaticists. Features: Focusses on the cross-disciplinary relation between computer science and biology and the role of machine learning methods in resolving complex problems in bioinformatics Provides a comprehensive and balanced blend of topics and applications using various advanced algorithms Presents cutting-edge research methodologies in the area of AI methods when applied to bioinformatics and innovative solutions Discusses the AI/ML techniques, their use, and their potential for use in common and future bioinformatics applications Includes recent achievements in AI and bioinformatics contributed by a global team of researchers
Association Analysis Techniques and Applications in Bioinformatics
Advances in experimental technologies have given rise to tremendous amounts of biology data. This not only offers valuable sources of data to help understand biological evolution and functional mechanisms, but also poses challenges for accurate and effective data analysis. This book offers an essential introduction to the theoretical and practical aspects of association analysis, including data pre-processing, data mining methods/algorithms, and tools that are widely applied for computational biology. It covers significant recent advances in the field, both foundational and application-oriented, helping readers understand the basic principles and emerging techniques used to discover interesting association patterns in diverse and heterogeneous biology data, such as structure-function correlations, and complex networks with gene/protein regulation. The main results and approaches are described in an easy-to-follow way and accompanied by sufficientreferences and suggestions for future research. This carefully edited monograph is intended to provide investigators in the fields of data mining, machine learning, artificial intelligence, and bioinformatics with a profound guide to the role of association analysis in computational biology. It is also very useful as a general source of information on association analysis, and as an overall accompanying course book and self-study material for graduate students and researchers in both computer science and bioinformatics.