Khalid Raza – författare

Translational Bioinformatics in Healthcare and Medicine offers an overview of main principles of bioinformatics, biological databases, clinical informatics, health informatics, viroinformatics and real-case applications of translational bioinformatics in healthcare. Written by experts from both technology and clinical sides, the content brings together essential knowledge to make the best of recent advancements of the field.

The book discusses topics such as next generation sequence analysis, genomics in clinical care, IoT applications, blockchain technology, patient centered interoperability of EHR, health data mining, and translational bioinformatics methods for drug discovery and drug repurposing. In addition, it discusses the role of bioinformatics in cancer research and viroinformatics approaches to counter viral diseases through informatics.

This is a valuable resource for bioinformaticians, clinicians, healthcare professionals, graduate students and several members of biomedical field who are interested in learning more about how bioinformatics can impact in their research and practice.

Covers recent advancements in translational bioinformatics and its healthcare applications Discusses integrative and multidisciplinary approaches to U-healthcare systems development and management Bridges the gap among various knowledge domains in the field, integrating both technological and clinical knowledge into practical content

Translational bioinformatics (TBI) involves development of storage, analytics, and advanced computational methods to harvest knowledge from voluminous biomedical and genomic data into 4P healthcare (proactive, predictive, preventive, and participatory). Translational Bioinformatics Applications in Healthcare offers a detailed overview on concepts of TBI, biological and clinical databases, clinical informatics, and pertinent real-case applications. It further illustrates recent advancements, tools, techniques, and applications of TBI in healthcare, including Internet of Things (IoT) potential, toxin databases, medical image analysis and telemedicine applications, analytics of COVID-19 CT images, viroinformatics and viral diseases, and COVID-19–related research. Covers recent technologies such as Blockchain, IoT, and Big data analytics in bioinformatics Presents the role of translational bioinformatic methods in the field of viroinformatics, as well as in drug development and repurposing Includes translational healthcare and NGS for clinical applications Illustrates translational medicine systems and their applications in better healthcare Explores medical image analysis with focus on CT images and novel coronavirus disease detectionAimed at researchers and graduate students in computational biology, data mining and knowledge discovery, algorithms and complexity, and interdisciplinary fields of studies, including bioinformatics, health-informatics, biostatistics, biomedical engineering, and viroinformatics.Khalid Raza is an Assistant Professor, the Department of Computer Science, Jamia Millia Islamia (Central University), New Delhi. His research interests include translational bioinformatics, computational intelligence methods and its applications in bioinformatics, viroinformatics, and health informatics.Nilanjan Dey is an Associate Professor, the Department of Computer Science and Engineering, JIS University, Kolkata, India. His research interests include medical imaging, machine learning, computer-aided diagnosis, and data mining.

Translational bioinformatics (TBI) involves development of storage, analytics, and advanced computational methods to harvest knowledge from voluminous biomedical and genomic data into 4P healthcare (proactive, predictive, preventive, and participatory). Translational Bioinformatics Applications in Healthcare offers a detailed overview on concepts of TBI, biological and clinical databases, clinical informatics, and pertinent real-case applications. It further illustrates recent advancements, tools, techniques, and applications of TBI in healthcare, including Internet of Things (IoT) potential, toxin databases, medical image analysis and telemedicine applications, analytics of COVID-19 CT images, viroinformatics and viral diseases, and COVID-19–related research. Covers recent technologies such as Blockchain, IoT, and Big data analytics in bioinformatics Presents the role of translational bioinformatic methods in the field of viroinformatics, as well as in drug development and repurposing Includes translational healthcare and NGS for clinical applications Illustrates translational medicine systems and their applications in better healthcare Explores medical image analysis with focus on CT images and novel coronavirus disease detectionAimed at researchers and graduate students in computational biology, data mining and knowledge discovery, algorithms and complexity, and interdisciplinary fields of studies, including bioinformatics, health-informatics, biostatistics, biomedical engineering, and viroinformatics.Khalid Raza is an Assistant Professor, the Department of Computer Science, Jamia Millia Islamia (Central University), New Delhi. His research interests include translational bioinformatics, computational intelligence methods and its applications in bioinformatics, viroinformatics, and health informatics.Nilanjan Dey is an Associate Professor, the Department of Computer Science and Engineering, JIS University, Kolkata, India. His research interests include medical imaging, machine learning, computer-aided diagnosis, and data mining.

Deep Learning Applications in Translational Bioinformatics, a new volume in the Advances in Ubiquitous Sensing Application for Healthcare series, offers a detailed overview of basic bioinformatics, deep learning, and various applications of deep learning in translational bioinformatics, including deep learning ensembles, deep learning in protein classification, detection of various diseases, prediction of antiviral peptides, identification of antibiotic resistance, computer aided drug design and drug formulation. This new volume helps researchers working in the field of machine learning and bioinformatics foster future research and development.

Addresses the practical application of deep learning algorithms to a wide range of bioinformatics challengesPresents integrative and multidisciplinary approaches to ubiquitous healthcareIncludes case studies to illustrate the concepts discussed

Deep Learning in Genetics and Genomics: Vol. 2 (Advanced Applications) delves into the Deep Learning methods and their applications in various fields of studies, including genetics and genomics, bioinformatics, health informatics and medical informatics generating the momentum of today's developments in the field. In 25 chapters this title covers advanced applications in the field which includes deep learning in predictive medicines), analysis of genetic and clinical features, transcriptomics and gene expression patterns analysis, clinical decision support in genetic diagnostics, deep learning in personalised genomics and gene editing, and understanding genetic discoveries through Explainable AI. Further, it also covers various deep learning-based case studies, making this book a unique resource for wider, deeper, and in-depth coverage of recent advancement in deep learning based approaches. This volume is not only a valuable resource for health educators, clinicians, and healthcare professionals but also to graduate students of genetics, genomics, biology, biostatistics, biomedical sciences, bioinformatics, and interdisciplinary sciences.Embraces the potential that deep learning holds for understanding genome biologyEncourages further advances in this area, extending to all aspects of genomics researchProvides Deep Learning algorithms in genetic and genomic research

Deep Learning in Genetics and Genomics vol. 1, Foundations and Applications, the intersection of deep learning and genetics opens up new avenues for advancing our understanding of the genetic code, gene regulation, and the broader genomics landscape. The book not only covers the most up-to-date advancements in the field of deep learning in genetics and genomics, but also a wide spectrum of (sub) topics including medical and clinical genetics, predictive medicine, transcriptomic, and gene expression studies. In 21 chapters Deep Learning in Genetics and Genomics vol. 1, Foundations and Applications describes how AI and DL have become increasingly useful in genetics and genomics research where both play a crucial role by accelerating research, improving the understanding of the human genome, and enabling personalized healthcare. From the fundamentals concepts and practical applications of deep learning algorithms to a wide range of challenging problems from genetics and genomics, Deep Learning in Genetics and Genomics vol. 1, Foundations and Applications creates a better knowledge of the biological and genetics mechanisms behind disease illnesses and improves the forecasting abilities using the different methodologies described. This title offers a unique resource for wider, deeper, and in-depth coverage of recent advancement in deep learning-based approaches in genetics and genomics, helping researchers process and interpret vast amounts of genetic data, identify patterns, and make discoveries that would be challenging or impossible using traditional methods.Brings together fundamental concepts of genetics, genomics, and deep learningIncludes how to build background of solution methodologies and design of mathematical and logical algorithmsDelves into the intersection of deep learning and genetics, offering a comprehensive exploration of how deep learning techniques can be applied to various aspects of genomics

Artificial Intelligence in Precision Drug Design: Advanced Applications showcases how artificial intelligence (AI) is revolutionizing modern drug discovery and development. Building upon the foundational principles established in Volume 1, this book dives into real-world applications where AI accelerates innovation, enhances predictive accuracy, and enables breakthrough therapeutics. Featuring contributions from leading global researchers and practitioners, the book explores machine learning, deep learning, and network-based approaches applied to complex biomedical challenges. Key areas include AI driven drug repurposing, combination therapies, immunotherapy, vaccine design, quantum computing, and the integration of large language models in drug discovery. Additional chapters highlight predictive modeling using electronic health records, AI-powered medical imaging, and explainable AI for structure-based drug design. What sets this volume apart is its emphasis on practical impact, demonstrating how data, computation, and interdisciplinary collaboration converge to advance precision medicine. Designed for scientists, clinicians, educators, and students, it serves as both a comprehensive reference and a source of inspiration for leveraging AI to transform healthcare.. Focuses on real-world AI applications in drug design and development provides comprehensive coverage of advanced techniques: deep learning, network-based models, and quantum computing.. Includes case studies on drug repurposing, combination therapies, immunotherapy, and vaccine development.. Provides insights into predictive modeling, AI-driven medical imaging, and explainable AI.. Highlights practical impact and interdisciplinary collaboration in precision medicine.

Artificial Intelligence in Precision Drug Design, Volume 2: Advanced Applications explores the transformative role of AI in modern drug discovery and development, presenting cutting-edge research and methodologies that integrate machine learning, network pharmacology, and computational modeling to accelerate therapeutic innovation. It covers a wide range of applications, including AI-driven drug repurposing, combination therapies, cancer immunotherapy, neuroscience, vaccine development, and molecular dynamics, but also delves into emerging technologies such as quantum computing, large language models, and graph neural networks. Designed for academics, this volume provides researchers, clinicians, and graduate students with a resource on the latest AI methodologies in precision medicine.Users will find this to be a great resource that supports evidence-based innovation, fosters interdisciplinary collaboration, and equips them with insights to advance personalized healthcare solutions.Presents state-of-the-art research on AI-driven drug repurposing, combination therapies, vaccine development, and molecular modeling, offering a comprehensive view of current innovationsCovers cutting-edge AI applications across drug discoveryCombines machine learning, network pharmacology, quantum computing, and large language models to address complex biomedical challenges with precision and scalabilityEquips scholars, clinicians, and graduate students with practical insights and frameworks to accelerate personalized medicine and foster interdisciplinary collaboration

Recent Advances in Molecular Docking: Methods, Tools, and Applications offers an authoritative guide to modern molecular docking techniques, emphasizing the integration of artificial intelligence and machine learning. The volume covers foundational principles, computational workflows, and advanced scoring functions, providing researchers and practitioners with practical tools to improve drug-target interaction predictions. It highlights applications across pharmaceuticals, including cancer, neurological disorders, and infectious diseases, which are all supported by real-world case studies. Challenges in docking accuracy, reproducibility, validation, and ethical and regulatory considerations are also covered.Authored by international experts, it bridges theoretical knowledge with practical applications, making it an essential resource for computational biologists, pharmacologists, and drug developers aiming to leverage AI-driven docking in precision medicine.Explores state-of-the-art molecular docking methods that integrate AI and machine learningProvides practical guidance on docking workflows, scoring functions, and benchmarking toolsCovers applications in drug discovery, precision therapeutics, and multi-omics integrationIncludes case studies on cancer, neurological diseases, and infectious diseasesAddresses ethical, legal, and regulatory perspectives in computational drug design

This book serves as the official proceedings of the Third International Conference on Intelligent Computing and Cutting-edge Technologies (ICICCT 2025), showcasing a curated collection of ground-breaking research and innovations in the field of intelligent computing. This book is structured into well-defined sections, each addressing key themes of the conference: Foundations of Smart Computing: This book introduces the fundamental concepts, theories, and technologies that form the basis of intelligent computing. Topics include artificial intelligence, machine learning, IoT, and cloud computing. Emerging Technologies in Computing: Delving into state-of-the-art developments, this book covers transformative technologies such as quantum computing, blockchain, 5G, and digital twins, emphasizing their potential to revolutionize industries. Applications of Intelligent Computing: Focused on real-world applications, this book explores how smart systems are being leveraged in healthcare, smart cities, Industry 4.0, and environmental sustainability. Advanced Topics in Computing: Featuring specialized topics such as Explainable AI (XAI), Edge AI, cybersecurity, and human-centric computing, this book provides insights into emerging research areas. Future Trends and Challenges: This forward-looking section identifies promising research directions and discusses the societal and ethical challenges of deploying intelligent systems at scale.

This edited book provides a comprehensive exploration of the latest advancements and methodologies in signal processing for intelligent systems. The book serves as a multidisciplinary reference, integrating theoretical foundations, algorithmic developments, and real-world applications across diverse domains, including healthcare, agriculture, environmental monitoring, and human activity recognition. By consolidating recent advancements and cross-domain insights, this book bridges the gap between theory and practice in sensor signal processing for intelligent systems. It fosters interdisciplinary collaboration and stimulates further research in the development of more efficient and scalable intelligent systems for diverse applications.

The novel coronavirus disease 2019 (COVID-19) pandemic has posed a major threat to human life and health. This book is beneficial for interdisciplinary students, researchers, and professionals to understand COVID-19 and how computational intelligence can be used for the purpose of surveillance, control, prevention, prediction, diagnosis, and potential treatment of the disease. The book contains different aspects of COVID-19 that includes fundamental knowledge, epidemic forecast models, surveillance and tracking systems, IoT- and IoMT-based integrated systems for COVID-19, social network analysis systems for COVID-19, radiological images (CT, X-ray) based diagnosis system, and computational intelligence and in silico drug design and drug repurposing methods against COVID-19 patients. The contributing authors of this volume are experts in their fields and they are from various reputed universities and institutions across the world. This volume is a valuable and comprehensive resource forcomputer and data scientists, epidemiologists, radiologists, doctors, clinicians, pharmaceutical professionals, along with graduate and research students of interdisciplinary and multidisciplinary sciences.

The novel coronavirus disease 2019 (COVID-19) pandemic has posed a major threat to human life and health. This book is beneficial for interdisciplinary students, researchers, and professionals to understand COVID-19 and how computational intelligence can be used for the purpose of surveillance, control, prevention, prediction, diagnosis, and potential treatment of the disease. The book contains different aspects of COVID-19 that includes fundamental knowledge, epidemic forecast models, surveillance and tracking systems, IoT- and IoMT-based integrated systems for COVID-19, social network analysis systems for COVID-19, radiological images (CT, X-ray) based diagnosis system, and computational intelligence and in silico drug design and drug repurposing methods against COVID-19 patients. The contributing authors of this volume are experts in their fields and they are from various reputed universities and institutions across the world. This volume is a valuable and comprehensive resource forcomputer and data scientists, epidemiologists, radiologists, doctors, clinicians, pharmaceutical professionals, along with graduate and research students of interdisciplinary and multidisciplinary sciences.

This book encapsulates recent applications of CI methods in the field of computational oncology, especially cancer diagnosis, prognosis, and its optimized therapeutics. The cancer has been known as a heterogeneous disease categorized in several different subtypes.

This book encapsulates recent applications of CI methods in the field of computational oncology, especially cancer diagnosis, prognosis, and its optimized therapeutics. The cancer has been known as a heterogeneous disease categorized in several different subtypes.

This book encapsulates and occupies recent advances and state-of-the-art applications of nature-inspired computing (NIC) techniques in the field of bioinformatics and computational biology, which would aid medical sciences in various clinical applications.

This book encapsulates and occupies recent advances and state-of-the-art applications of nature-inspired computing (NIC) techniques in the field of bioinformatics and computational biology, which would aid medical sciences in various clinical applications.

This book provides a concise guide tailored for researchers, bioinformaticians, and enthusiasts eager to unravel the mysteries hidden within single-cell RNA sequencing (scRNA-seq) data using cutting-edge machine learning techniques.

This book is a comprehensive volume, which delves into the versatile world of Artificial Bee Colony (ABC) algorithms, their variants, and myriad applications in a wide range of fields. This book serves as a bridge between the theoretical foundations of ABC algorithms and their practical implementations across diverse domains.

This book is a comprehensive volume, which delves into the versatile world of Artificial Bee Colony (ABC) algorithms, their variants, and myriad applications in a wide range of fields. This book is designed to be an essential resource for researchers, practitioners, students, and anyone intrigued by the fascinating realm of swarm intelligence and optimization. This book serves as a bridge between the theoretical foundations of ABC algorithms and their practical implementations across diverse domains. The book offers a deep understanding of these algorithms and how they can be harnessed to tackle complex real-world challenges.

This comprehensive volume focuses on the latest advancements in Generative AI, including state-of-the-art techniques and models that are pushing the boundaries of what is possible.

Dieses Buch umfasst und beschäftigt sich mit den jüngsten Fortschritten und modernsten Anwendungen von naturinspirierten Computertechniken (NIC) im Bereich der Bioinformatik und der Computerbiologie, die die medizinischen Wissenschaften bei verschiedenen klinischen Anwendungen unterstützen können. Dieser Sammelband befasst sich mit den grundlegenden Anwendungen, dem Umfang und den Zukunftsperspektiven von NIC-Techniken in der Bioinformatik, einschließlich der Erstellung von Genomprofilen, der Klassifizierung von Genexpressionsdaten, der DNA-Berechnung, der System- und Netzwerkbiologie, der Lösung von Komplikationen bei personalisierten Therapien, der antimikrobiellen Resistenz bei bakteriellen Krankheitserregern und der computergestützten Entwicklung von Arzneimitteln, deren Entdeckung und Therapie. Darüber hinaus wird die Rolle von NIC-Techniken bei verschiedenen Krankheiten und Störungen behandelt, einschließlich Krebserkennung und -diagnose, Brustkrebs, Erkennung von Lungenkrankheiten, Krankheits-Biomarkern und Identifizierung potenzieller Therapeutika.

The book provides an overview of various autoimmune disorders and how artificial intelligence (AI) and machine learning will be used for the diagnosis, prognosis, and treatment of these disorders.