This detailed book provides an overview of various classes of computational techniques, including machine learning techniques, commonly used for evaluating kinetic parameters of biological systems.
This new edition explores lab protocols describing new techniques to study cytotoxic T-cells (CTLs), as well as chapters of a more general discursive nature, all with an emphasis on the use of systems biology in immunology.
This volume addresses the latest state-of-the-art systems biology-oriented approaches that--driven by big data and bioinformatics--are utilized by Computational Systems Biology, an interdisciplinary field that bridges experimental tools with computational tools to tackle complex questions at the frontiers of knowledge in medicine and biotechnology.
This volume focuses on a variety of in silico protocols of the latest bioinformatics tools and computational pipelines developed for neo-antigen identification and immune cell analysis from high-throughput sequencing data for cancer immunotherapy.
This thorough new edition explores web servers and software for protein structure prediction and modeling that are freely available to the academic community.
This book provides a comprehensive guide to scientists, engineers, and students that employ metabolomics in their work, with an emphasis on the understanding and interpretation of the data.
This manual provides insights into the theory and practical aspects of several biotechnological and biochemical techniques for plants, protozoa, nematodes, insects and fishes, as well as human samples.
Emerging Practices in Telehealth: Best Practices in a Rapidly Changing Field is an introduction to telehealth basics, best practices and implementation methods.
Computational Modeling of Infectious Disease: With Applications in Python provides an illustrated compendium of tools and tactics for analyzing infectious diseases using cutting-edge computational methods.
This volume details state-of-art eQTL analysis, where interdisciplinary researchers are provided both theoretical and practical guidance to eQTL analysis and interpretation.
This second edition provides new and updated chapters from expert researchers in the field detailing methods used to study the multi-facet deep sequencing data field.
This volume presents state-of-the art design, analysis and integration approaches for biomedical data including novel statistical models for a comprehensive and powerful synthesis and assessment of scientific evidence.
This volume introduces Translational Bioinformatics as it relates to therapeutic development, and addresses the techniques needed to effectively translate large data sets to relevant biological networks.
This meticulous book explores the leading methodologies, techniques, and tools for microarray data analysis, given the difficulty of harnessing the enormous amount of data.
This detailed collection explores genome-wide association studies (GWAS), which have revolutionized the investigation of complex traits over the past decade and have unveiled numerous useful genotype-phenotype associations in plants.
This book provides a single-source reference on the current state of the ribosome profiling method by describing experimental protocols for the quantitative analysis of translation in a variety of model organisms.
This volume focuses on computational modeling of the ecotoxicity of chemicals and presents applications of quantitative structure-activity relationship models (QSARs) in the predictive toxicology field in a regulatory context.
This thorough book provides a collection of techniques used in the emerging field of computational chemogenomics, which is an integration of chemoinformatics, bioinformatics, computer science, statistics, automated pattern recognition and modeling, database usage with data retrieval, and systems integration.
Molecular Modeling of Proteins, Second Edition provides a theoretical background of various methods available and enables non-specialists to apply methods to their problems by including updated chapters and new material not covered in the first edition.
This second edition volume expands on the previous edition with a look at the latest advances in techniques to study yeast and its core set of interactions, modules, architectures, and network dynamics that are common in all eukaryotes.
This volume explores the recent advancements in biomolecular simulations of proteins, small molecules, and nucleic acids, with a primary focus on classical molecular dynamics (MD) simulations at atomistic, coarse-grained, and quantum/ab-initio levels.
This thorough book covers the most recent proteomics techniques, databases, bioinformatics tools, and computational approaches that are used for the identification and functional annotation of proteins and their structure.
Exploring the 2-D gel mapping field, the chapters in this book are separated into four different categories: Part I talks about 2-D maps reproducibility and maps modeling; Part II describes the image analysis tools that provide spot volume datasets; Part III is about the statistical methods applied to spot volume datasets to identify candidate biomarkers; and Part IV discusses differential analysis from direct image analysis tools.
This volume details computational techniques for analyses of a wide range of biological contexts, providing an overview of the most up-to-date techniques used in the field.
This volume introduces bioinformatics research methods for proteins, with special focus on protein post-translational modifications (PTMs) and networks.
This second edition provides updated and expanded chapters covering a broad sampling of useful and current methods in the rapidly developing and expanding field of bioinformatics.
This volume guides readers through the field of systems medicine by defining the terminology, and describing how established computational methods form bioinformatics and systems biology can be taken forward to an integrative systems medicine approach.
This fully updated book collects numerous data mining techniques, reflecting the acceleration and diversity of the development of data-driven approaches to the life sciences.
