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Computational and Experimental Protein Variant Interpretation in the Era of Precision Medicine
One of the key goals in the postgenomic era is the elucidation of the mechanisms underlying the relationship between genotype and phenotype. In particular, understanding how human genetic and somatic variations are associated with diseases is still an open problem and its solution is a crucial issue for exploiting the possibilities offered by the modern sequencing techniques in the framework of precision and personalized medicine. The increasing amount of data generated by the sequencing initiatives calls for accurate and reliable computational approaches to predict the impact of mutations on the phenotype, and possibly for methods to correlate them with diseases. From the experimental point of view, disease-causing variants are supposed to directly affect protein function, protein stability as well as the kinetics and thermodynamics of protein-protein recognition, and robust validation at the molecular scale is necessary. This approach can be of invaluable help in facing new challenges such as the fast development of effective vaccines.
Research in Computational Molecular Biology
This book constitutes the proceedings of the 26th Annual Conference on Research in Computational Molecular Biology, RECOMB 2022, held in San Diego, CA, USA in May 2022. The 17 regular and 23 short papers presented were carefully reviewed and selected from 188 submissions. The papers report on original research in all areas of computational molecular biology and bioinformatics.
Computational Approaches in Biomaterials and Biomedical Engineering Applications
Computational Approaches in Bioengineering, Volume 2—Computational Approaches in Biomaterials and Biomedical Engineering Applications is a comprehensive and up-to-date resource that provides a broad overview of the use of computational methods in the fields of biomaterials and biomedical engineering. Written by a team of experts in the field of biomaterials and biomedical engineering, it provides a wealth of information on the use of computational methods in these fields. Furthermore, it explores emerging trends and discusses future directions and associated limitations in the field. Through thorough exploration and explanation, it showcases the latest research and advancements, offering v...
Computational Intelligence Methods for Bioinformatics and Biostatistics
This book constitutes revised selected papers from the 17th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2021, which was held virtually during November 15–17, 2021. The 19 papers included in these proceedings were carefully reviewed and selected from 26 submissions, and they focus on bioinformatics, computational biology, health informatics, cheminformatics, biotechnology, biostatistics, and biomedical imaging.
Gene Network Inference
This book presents recent methods for Systems Genetics (SG) data analysis, applying them to a suite of simulated SG benchmark datasets. Each of the chapter authors received the same datasets to evaluate the performance of their method to better understand which algorithms are most useful for obtaining reliable models from SG datasets. The knowledge gained from this benchmarking study will ultimately allow these algorithms to be used with confidence for SG studies e.g. of complex human diseases or food crop improvement. The book is primarily intended for researchers with a background in the life sciences, not for computer scientists or statisticians.
Nonlinear Dimensionality Reduction
This book describes established and advanced methods for reducing the dimensionality of numerical databases. Each description starts from intuitive ideas, develops the necessary mathematical details, and ends by outlining the algorithmic implementation. The text provides a lucid summary of facts and concepts relating to well-known methods as well as recent developments in nonlinear dimensionality reduction. Methods are all described from a unifying point of view, which helps to highlight their respective strengths and shortcomings. The presentation will appeal to statisticians, computer scientists and data analysts, and other practitioners having a basic background in statistics or computational learning.
Free Energy Calculations
Free energy constitutes the most important thermodynamic quantity to understand how chemical species recognize each other, associate or react. Examples of problems in which knowledge of the underlying free energy behaviour is required, include conformational equilibria and molecular association, partitioning between immiscible liquids, receptor-drug interaction, protein-protein and protein-DNA association, and protein stability. This volume sets out to present a coherent and comprehensive account of the concepts that underlie different approaches devised for the determination of free energies. The reader will gain the necessary insight into the theoretical and computational foundations of th...
Accelerated Predictive Stability (APS)
Accelerated Predictive Stability (APS): Fundamentals and Pharmaceutical Industry Practices provides coverage of both the fundamental principles and pharmaceutical industry applications of the APS approach. Fundamental chapters explain the scientific basis of the APS approach, while case study chapters from many innovative pharmaceutical companies provide a thorough overview of the current status of APS applications in the pharmaceutical industry. In addition, up-to-date experiences in utilizing APS data for regulatory submissions in many regions and countries highlight the potential of APS in support of registration stability testing for certain regulatory submissions. This book provides hig...
