In the evolving environment of bioinformatics, genomics, and computational biology, academic scholars are facing a challenging challenge – keeping informed about the latest research trends and findings. With unprecedented advancements in sequencing technologies, computational algorithms, and machine learning, these fields have become indispensable tools for drug discovery, disease research, genome sequencing, and more. As scholars strive to decode the language of DNA, predict protein structures, and navigate the complexities of biological data analysis, the need for a comprehensive and up-to-date resource becomes paramount. The Research Anthology on Bioinformatics, Genomics, and Computational Biology is a collection of a carefully curated selection of chapters that serves as the solution to the pressing challenge of keeping pace with the dynamic advancements in these critical disciplines. This anthology is designed to address the informational gap by providing scholars with a consolidated and authoritative source that sheds light on critical issues, innovative theories, and transformative developments in the field. It acts as a single reference point, offering insights into conceptual, methodological, technical, and managerial issues while also providing a glimpse into emerging trends and future opportunities.

Machine learning continues to have myriad applications across industries and fields. To ensure this technology is utilized appropriately and to its full potential, organizations must better understand exactly how and where it can be adapted. Further study on the applications of machine learning is required to discover its best practices, challenges, and strategies. The Research Anthology on Machine Learning Techniques, Methods, and Applications provides a thorough consideration of the innovative and emerging research within the area of machine learning. The book discusses how the technology has been used in the past as well as potential ways it can be used in the future to ensure industries continue to develop and grow. Covering a range of topics such as artificial intelligence, deep learning, cybersecurity, and robotics, this major reference work is ideal for computer scientists, managers, researchers, scholars, practitioners, academicians, instructors, and students.

With the influx of internet and mobile technology usage, many medical institutions—from doctor’s offices to hospitals—have implemented new online technologies for the storage and access of health data as well as the monitoring of patient health. Telehealth was particularly useful during the COVID-19 pandemic, which monumentally increased its everyday usage. However, this transition of health data has increased privacy risks, and cyber criminals and hackers may have increased access to patient personal data. Medical staff and administrations must remain up to date on the new technologies and methods in securing these medical systems and records. The Research Anthology on Securing Medical Systems and Records discusses the emerging challenges in healthcare privacy as well as the technologies, methodologies, and emerging research in securing medical systems and enhancing patient privacy. It provides information on the implementation of these technologies as well as new avenues of medical security research. Covering topics such as biomedical imaging, internet of things, and watermarking, this major reference work is a comprehensive resource for security analysts, data scientists, hospital administrators, leaders in healthcare, medical professionals, health information managers, medical professionals, mobile application developers, security professionals, technicians, students, libraries, researchers, and academicians.

Artificial neural networks (ANNs) present many benefits in analyzing complex data in a proficient manner. As an effective and efficient problem-solving method, ANNs are incredibly useful in many different fields. From education to medicine and banking to engineering, artificial neural networks are a growing phenomenon as more realize the plethora of uses and benefits they provide. Due to their complexity, it is vital for researchers to understand ANN capabilities in various fields. The Research Anthology on Artificial Neural Network Applications covers critical topics related to artificial neural networks and their multitude of applications in a number of diverse areas including medicine, finance, operations research, business, social media, security, and more. Covering everything from the applications and uses of artificial neural networks to deep learning and non-linear problems, this book is ideal for computer scientists, IT specialists, data scientists, technologists, business owners, engineers, government agencies, researchers, academicians, and students, as well as anyone who is interested in learning more about how artificial neural networks can be used across a wide range of fields.

Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, Three Volume Set combines elements of computer science, information technology, mathematics, statistics and biotechnology, providing the methodology and in silico solutions to mine biological data and processes. The book covers Theory, Topics and Applications, with a special focus on Integrative –omics and Systems Biology. The theoretical, methodological underpinnings of BCB, including phylogeny are covered, as are more current areas of focus, such as translational bioinformatics, cheminformatics, and environmental informatics. Finally, Applications provide guidance for commonly asked questions. This major reference work spans basic and cutting-edge methodologies authored by leaders in the field, providing an invaluable resource for students, scientists, professionals in research institutes, and a broad swath of researchers in biotechnology and the biomedical and pharmaceutical industries. Brings together information from computer science, information technology, mathematics, statistics and biotechnology Written and reviewed by leading experts in the field, providing a unique and authoritative resource Focuses on the main theoretical and methodological concepts before expanding on specific topics and applications Includes interactive images, multimedia tools and crosslinking to further resources and databases

The understanding of biological complexity has been greatly facilitated by cross-disciplinary, holistic approaches that allow insights into the function and regulation of biological processes that cannot be captured by dissecting them into their individual components. In addition, the development of novel tools has dramatically increased our ability to interrogate information at the nucleic acid, protein and metabolite level. The integration and interpretation of disparate data sets, however, still remain a major challenge in systems biology. Roots provide an excellent model for studying physiological, developmental, and metabolic processes. The availability of genetic resources, along with sequenced genomes has allowed important discoveries in root biochemistry, development and function. Roots are transparent, allowing optical investigation of gene activity in individual cells and experimental manipulation. In addition, the predictable fate of cells emerging from the root meristem and the continuous development of roots throughout the life of the plant, which permits simultaneous observation of different developmental stages, provide ideal premises for the analysis of growth and differentiation. Moreover, a genetically fixed cellular organization allows for studying the utilization of positional information and other non-cell-autonomous phenomena, which are of utmost importance in plant development. Although their ontogeny is largely invariant under standardized experimental conditions, roots possess an extraordinary capacity to respond to a plethora of environmental signals, resulting in distinct phenotypic readouts. This high phenotypic plasticity allows research into acclimative and adaptive strategies, the understanding of which is crucial for germplasm enhancement and crop improvement. With the aim of providing a current snapshot on the function and development of roots at the systems level, this Research Topic collated original research articles, methods articles, reviews, mini reviews and perspective, opinion and hypotheses articles that communicate breakthroughs in root biology, as well as recent advances in research technologies and data analysis.

The enormous complexity of biological systems at the molecular level must be answered with powerful computational methods. Computational biology is a young field, but has seen rapid growth and advancement over the past few decades. Surveying the progress made in this multidisciplinary field, the Handbook of Computational Molecular Biology offers comprehensive, systematic coverage of the various techniques and methodologies currently available. Accomplished researcher Srinivas Aluru leads a team of experts from around the world to produce this groundbreaking, authoritative reference. With discussions ranging from fundamental concepts to practical applications, this book details the algorithms necessary to solve novel problems and manage the massive amounts of data housed in biological databases throughout the world. Divided into eight sections for convenient searching, the handbook covers methods and algorithms for sequence alignment, string data structures, sequence assembly and clustering, genome-scale computational methods in comparative genomics, evolutionary and phylogenetic trees, microarrays and gene expression analysis, computational methods in structural biology, and bioinformatics databases and data mining. The Handbook of Computational Molecular Biology is the first resource to integrate coverage of the broad spectrum of topics in computational biology and bioinformatics. It supplies a quick-reference guide for easy implementation and provides a strong foundation for future discoveries in the field.

This textbook introduces fundamental concepts of bioinformatics and computational biology to the students and researchers in biology, medicine, veterinary science, agriculture, and bioengineering . The respective chapters provide detailed information on biological databases, sequence alignment, molecular evolution, next-generation sequencing, systems biology, and statistical computing using R. The book also presents a case-based discussion on clinical, veterinary, agricultural bioinformatics, and computational bioengineering for application-based learning in the respective fields. Further, it offers readers guidance on reconstructing and analysing biological networks and highlights computational methods used in systems medicine and genome-wide association mapping of diseases. Given its scope, this textbook offers an essential introductory book on bioinformatics and computational biology for undergraduate and graduate students in the life sciences, botany, zoology, physiology, biotechnology, bioinformatics, and genomic science as well as systems biology, bioengineering and the agricultural, and veterinary sciences.

Bioinformatics in Agriculture: Next Generation Sequencing Era is a comprehensive volume presenting an integrated research and development approach to the practical application of genomics to improve agricultural crops. Exploring both the theoretical and applied aspects of computational biology, and focusing on the innovation processes, the book highlights the increased productivity of a translational approach. Presented in four sections and including insights from experts from around the world, the book includes: Section I: Bioinformatics and Next Generation Sequencing Technologies; Section II: Omics Application; Section III: Data mining and Markers Discovery; Section IV: Artificial Intelligence and Agribots. Bioinformatics in Agriculture: Next Generation Sequencing Era explores deep sequencing, NGS, genomic, transcriptome analysis and multiplexing, highlighting practices forreducing time, cost, and effort for the analysis of gene as they are pooled, and sequenced. Readers will gain real-world information on computational biology, genomics, applied data mining, machine learning, and artificial intelligence. This book serves as a complete package for advanced undergraduate students, researchers, and scientists with an interest in bioinformatics. - Discusses integral aspects of molecular biology and pivotal tool sfor molecular breeding - Enables breeders to design cost-effective and efficient breeding strategies - Provides examples ofinnovative genome-wide marker (SSR, SNP) discovery - Explores both the theoretical and practical aspects of computational biology with focus on innovation processes - Covers recent trends of bioinformatics and different tools and techniques