This book describes the bioinformatics research field, from its historical roots to the cutting-edge technologies. Many readers can discover the power of next-generation sequencing and genomic data analysis, uncover the secrets of single-cell genomics and transcriptomics, explore the metagenomics and microbiome analysis, and predict the protein structures using structural bioinformatics. Several case studies witnessing the fusion of bioinformatics and artificial intelligence, driving insights from vast biological datasets have also been explored. Other important aspects listed in the book are integrating the omics data for a holistic view of biological systems; experiencing the future of medicine with precision healthcare and personalized treatments; accelerating drug discovery and repurposing through computational approaches; agricultural genomics; and exploring the role of immunoinformatics in designing effective vaccines against infectious diseases.

This is a multidisciplinary edited book various fields of researchers publish here own articles. This book having 6 editorial board members including chief editor, editors and co-editors. This is volume 2 book in future many volumes will publish we planed -coordinator Mr. S. Ramesh kumar

A Biologist’s Guide to Artificial Intelligence: Building the Foundations of Artificial Intelligence and Machine Learning for Achieving Advancements in Life Sciences provides an overview of the basics of Artificial Intelligence for life science biologists. In 14 chapters/sections, readers will find an introduction to Artificial Intelligence from a biologist’s perspective, including coverage of AI in precision medicine, disease detection, and drug development. The book also gives insights into the AI techniques used in biology and the applications of AI in food, and in environmental, evolutionary, agricultural, and bioinformatic sciences. Final chapters cover ethical issues surrounding AI and the impact of AI on the future. This book covers an interdisciplinary area and is therefore is an important subject matter resource and reference for researchers in biology and students pursuing their degrees in all areas of Life Sciences. It is also a useful title for the industry sector and computer scientists who would gain a better understanding of the needs and requirements of biological sciences and thus better tune the algorithms. Helps biologists succeed in understanding the concepts of Artificial Intelligence and machine learning Equips with new data mining strategies an easy interface into the world of Artificial Intelligence Enables researchers to enhance their own sphere of researching Artificial Intelligence

The book analyzes the combination of intelligent data analytics with the intricacies of biological data that has become a crucial factor for innovation and growth in the fast-changing field of bioinformatics and biomedical systems. Intelligent Data Analytics for Bioinformatics and Biomedical Systems delves into the transformative nature of data analytics for bioinformatics and biomedical research. It offers a thorough examination of advanced techniques, methodologies, and applications that utilize intelligence to improve results in the healthcare sector. With the exponential growth of data in these domains, the book explores how computational intelligence and advanced analytic techniques can be harnessed to extract insights, drive informed decisions, and unlock hidden patterns from vast datasets. From genomic analysis to disease diagnostics and personalized medicine, the book aims to showcase intelligent approaches that enable researchers, clinicians, and data scientists to unravel complex biological processes and make significant strides in understanding human health and diseases. This book is divided into three sections, each focusing on computational intelligence and data sets in biomedical systems. The first section discusses the fundamental concepts of computational intelligence and big data in the context of bioinformatics. This section emphasizes data mining, pattern recognition, and knowledge discovery for bioinformatics applications. The second part talks about computational intelligence and big data in biomedical systems. Based on how these advanced techniques are utilized in the system, this section discusses how personalized medicine and precision healthcare enable treatment based on individual data and genetic profiles. The last section investigates the challenges and future directions of computational intelligence and big data in bioinformatics and biomedical systems. This section concludes with discussions on the potential impact of computational intelligence on addressing global healthcare challenges. Audience Intelligent Data Analytics for Bioinformatics and Biomedical Systems is primarily targeted to professionals and researchers in bioinformatics, genetics, molecular biology, biomedical engineering, and healthcare. The book will also suit academicians, students, and professionals working in pharmaceuticals and interpreting biomedical data.

Publisher’s note: This is a 2nd edition due to an article retraction.

There has been renewed interest in soil and soil science in recent years as the recognition that biogeochemical processes that occur at the Earth's surface influence global climate change, land degradation and remediation, the fate and transport of nutrients and contaminants, soil and water conservation, soil and water quality, food sufficiency and safety, and many other issues pertinent to the stewardship and conservation of land and water resources. In some areas of the Earth we have approached near irreversible soil conditions that may threaten the existence of future generations. Understanding the long-term implications of decreased soil quality and addressing the aforementioned challenges will require new information based on advances and breakthroughs in soil science research that need to be effectively communicated to stakeholders, policy makers, and the general public. On December 12-14, 2005, the National Academies convened the Frontiers in Soil Science Research Workshop, summarized in this volume, to identify emerging areas for research in soil science by addressing the interaction of soil science subdisciplines, collaborative research with other disciplines, and the use of new technologies in research. The workshop focused around seven key questions addressing research frontiers for the individual soil science disciplines, and also addressing the need for integration across soil science with other disciplines.

Why are cutting-edge data science techniques such as bioinformatics, few-shot learning, and zero-shot learning underutilized in the world of biological sciences?. In a rapidly advancing field, the failure to harness the full potential of these disciplines limits scientists’ ability to unlock critical insights into biological systems, personalized medicine, and biomarker identification. This untapped potential hinders progress and limits our capacity to tackle complex biological challenges. The solution to this issue lies within the pages of Applying Machine Learning Techniques to Bioinformatics. This book serves as a powerful resource, offering a comprehensive analysis of how these emerging disciplines can be effectively applied to the realm of biological research. By addressing these challenges and providing in-depth case studies and practical implementations, the book equips researchers, scientists, and curious minds with the knowledge and techniques needed to navigate the ever-changing landscape of bioinformatics and machine learning within the biological sciences.