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Protein Function Prediction
This fully updated volume explores a wide array of new and state-of-the-art tools and resources for protein function prediction. Beginning with in-depth overviews of essential underlying computational techniques, such as machine learning, multi-task learning, protein language models, and deep learning, the book continues by covering specific tools for protein function prediction, ranging from gene ontology-term predictions to the predictions of binding sites, protein localization and solubility, signal peptides, intrinsic disorder, and intrinsically disordered binding regions, as well as presenting databases that address protein moonlighting and protein binding. Written for the highly succes...
Protein Supersecondary Structures
This new edition delves into the latest developments in the field and new techniques used to study secondary and supersecondary structures (SSS) in proteins. Beyond the tremendous advances in the field from the AI-based AlphaFold algorithm, researchers continue to untangle how specific structures and protein folds come to be, and these chapters contain numerous techniques to further pursue this study. Written for the highly successful Methods in Molecular Biology series, chapters contain the kind of detailed implementation advice needed to ensure effective results in the lab. Authoritative and practical, Protein Supersecondary Structures: Methods and Protocols, Third Edition serves as a valuable resource for researchers exploring the relationship between amino acids sequences and protein structures, the evolution of proteins, and the dynamics of protein formation.
Cybersecurity, Cybercrimes, and Smart Emerging Technologies
This book presents cutting-edge research and advancements in the rapidly evolving fields of cybersecurity, cybercrimes, and smart emerging technologies. It serves as a comprehensive reference guide for the latest trends and challenges in securing our digital world. It highlights critical themes such as the application of AI and machine learning in threat detection and automation, the security implications of blockchain and distributed ledger technologies, safeguarding critical infrastructure and the IoT, addressing data privacy and governance, and advancing malware analysis and detection techniques. It also delves into technological breakthroughs in deep learning for fake account detection, blockchain for secure data exchange, DDoS mitigation strategies, and novel approaches to malware analysis. These findings provide valuable insights into current and emerging cyber threats and effective countermeasures. This book is an essential resource for researchers, cybersecurity professionals, policymakers, and anyone seeking to understand the complex landscape of cybersecurity in the digital age.
Machine Learning In Bioinformatics Of Protein Sequences: Algorithms, Databases And Resources For Modern Protein Bioinformatics
Machine Learning in Bioinformatics of Protein Sequences guides readers around the rapidly advancing world of cutting-edge machine learning applications in the protein bioinformatics field. Edited by bioinformatics expert, Dr Lukasz Kurgan, and with contributions by a dozen of accomplished researchers, this book provides a holistic view of the structural bioinformatics by covering a broad spectrum of algorithms, databases and software resources for the efficient and accurate prediction and characterization of functional and structural aspects of proteins. It spotlights key advances which include deep neural networks, natural language processing-based sequence embedding and covers a wide range of predictions which comprise of tertiary structure, secondary structure, residue contacts, intrinsic disorder, protein, peptide and nucleic acids-binding sites, hotspots, post-translational modification sites, and protein function. This volume is loaded with practical information that identifies and describes leading predictive tools, useful databases, webservers, and modern software platforms for the development of novel predictive tools.
Computational Methods for Predicting Post-Translational Modification Sites
This volume describes computational approaches to predict multitudes of PTM sites. Chapters describe in depth approaches on algorithms, state-of-the-art Deep Learning based approaches, hand-crafted features, physico-chemical based features, issues related to obtaining negative training, sequence-based features, and structure-based features. Written in the format of the highly successful Methods in Molecular Biology series, each chapter includes an introduction to the topic, lists necessary materials and reagents, includes tips on troubleshooting and known pitfalls, and step-by-step, readily reproducible protocols. Authoritative and cutting-edge, Authoritative and cutting-edge, Computational Methods for Predicting Post-Translational Modification Sites aims to be a useful guide for researchers who are interested in the field of PTM site prediction.
Large Language Models (LLMs) in Protein Bioinformatics
This book presents a comprehensive collection of methods, resources, and studies that use large language models (LLMs) in the field of protein bioinformatics. Reflecting the swift pace of LLM development today, the volume delves into numerous LLM-based tools to investigate proteins science, from protein language models to the prediction of protein-ligand binding sites. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detailed implementation advice to ensure success in future research. Authoritative and practical, Large Language Models (LLMs) in Protein Bioinformatics serves as an ideal guide for scientists seeking to tap into the potential of artificial intelligence in this vital area of biological study.
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