Bayesian Methods In Structural Bioinformatics
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Bayesian Methods In Structural Bioinformatics
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Author : Thomas Hamelryck
language : en
Publisher: Springer
Release Date : 2012-03-23
Bayesian Methods In Structural Bioinformatics written by Thomas Hamelryck and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-03-23 with Medical categories.
This book is an edited volume, the goal of which is to provide an overview of the current state-of-the-art in statistical methods applied to problems in structural bioinformatics (and in particular protein structure prediction, simulation, experimental structure determination and analysis). It focuses on statistical methods that have a clear interpretation in the framework of statistical physics, rather than ad hoc, black box methods based on neural networks or support vector machines. In addition, the emphasis is on methods that deal with biomolecular structure in atomic detail. The book is highly accessible, and only assumes background knowledge on protein structure, with a minimum of mathematical knowledge. Therefore, the book includes introductory chapters that contain a solid introduction to key topics such as Bayesian statistics and concepts in machine learning and statistical physics.
Advance In Structural Bioinformatics
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Author : Dongqing Wei
language : en
Publisher: Springer
Release Date : 2014-11-11
Advance In Structural Bioinformatics written by Dongqing Wei and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-11 with Science categories.
This text examines in detail mathematical and physical modeling, computational methods and systems for obtaining and analyzing biological structures, using pioneering research cases as examples. As such, it emphasizes programming and problem-solving skills. It provides information on structure bioinformatics at various levels, with individual chapters covering introductory to advanced aspects, from fundamental methods and guidelines on acquiring and analyzing genomics and proteomics sequences, the structures of protein, DNA and RNA, to the basics of physical simulations and methods for conformation searches. This book will be of immense value to researchers and students in the fields of bioinformatics, computational biology and chemistry. Dr. Dongqing Wei is a Professor at the Department of Bioinformatics and Biostatistics, College of Life Science and Biotechnology, Shanghai Jiaotong University, Shanghai, China. His research interest is in the general area of structural bioinformatics.
Applied Directional Statistics
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Author : Christophe Ley
language : en
Publisher: CRC Press
Release Date : 2018-09-03
Applied Directional Statistics written by Christophe Ley and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-03 with Mathematics categories.
This book collects important advances in methodology and data analysis for directional statistics. It is the companion book of the more theoretical treatment presented in Modern Directional Statistics (CRC Press, 2017). The field of directional statistics has received a lot of attention due to demands from disciplines such as life sciences or machine learning, the availability of massive data sets requiring adapted statistical techniques, and technological advances. This book covers important progress in bioinformatics, biology, astrophysics, oceanography, environmental sciences, earth sciences, machine learning and social sciences.
Geometry Driven Statistics
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Author : Ian L. Dryden
language : en
Publisher: John Wiley & Sons
Release Date : 2015-09-03
Geometry Driven Statistics written by Ian L. Dryden and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-09-03 with Mathematics categories.
A timely collection of advanced, original material in the area of statistical methodology motivated by geometric problems, dedicated to the influential work of Kanti V. Mardia This volume celebrates Kanti V. Mardia's long and influential career in statistics. A common theme unifying much of Mardia’s work is the importance of geometry in statistics, and to highlight the areas emphasized in his research this book brings together 16 contributions from high-profile researchers in the field. Geometry Driven Statistics covers a wide range of application areas including directional data, shape analysis, spatial data, climate science, fingerprints, image analysis, computer vision and bioinformatics. The book will appeal to statisticians and others with an interest in data motivated by geometric considerations. Summarizing the state of the art, examining some new developments and presenting a vision for the future, Geometry Driven Statistics will enable the reader to broaden knowledge of important research areas in statistics and gain a new appreciation of the work and influence of Kanti V. Mardia.
Directional Estimation For Robotic Beating Heart Surgery
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Author : Kurz, Gerhard
language : en
Publisher: KIT Scientific Publishing
Release Date : 2015-05-26
Directional Estimation For Robotic Beating Heart Surgery written by Kurz, Gerhard and has been published by KIT Scientific Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-05-26 with Electronic computers. Computer science categories.
In robotic beating heart surgery, a remote-controlled robot can be used to carry out the operation while automatically canceling out the heart motion. The surgeon controlling the robot is shown a stabilized view of the heart. First, we consider the use of directional statistics for estimation of the phase of the heartbeat. Second, we deal with reconstruction of a moving and deformable surface. Third, we address the question of obtaining a stabilized image of the heart.
Bayesian Analysis With Python
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Author : Osvaldo Martin
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-12-26
Bayesian Analysis With Python written by Osvaldo Martin and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-26 with Computers categories.
Bayesian modeling with PyMC3 and exploratory analysis of Bayesian models with ArviZ Key FeaturesA step-by-step guide to conduct Bayesian data analyses using PyMC3 and ArviZA modern, practical and computational approach to Bayesian statistical modelingA tutorial for Bayesian analysis and best practices with the help of sample problems and practice exercises.Book Description The second edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models. The main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others. By the end of the book, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. After reading the book you will be better prepared to delve into more advanced material or specialized statistical modeling if you need to. What you will learnBuild probabilistic models using the Python library PyMC3 Analyze probabilistic models with the help of ArviZ Acquire the skills required to sanity check models and modify them if necessary Understand the advantages and caveats of hierarchical modelsFind out how different models can be used to answer different data analysis questionsCompare models and choose between alternative onesDiscover how different models are unified from a probabilistic perspective Think probabilistically and benefit from the flexibility of the Bayesian frameworkWho this book is for If you are a student, data scientist, researcher, or a developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory so no previous statistical knowledge is required, although some experience in using Python and NumPy is expected.
Handbook Of Research On Trends In The Diagnosis And Treatment Of Chronic Conditions
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Author : Fotiadis, Dimitrios I.
language : en
Publisher: IGI Global
Release Date : 2015-08-26
Handbook Of Research On Trends In The Diagnosis And Treatment Of Chronic Conditions written by Fotiadis, Dimitrios I. and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-08-26 with Health & Fitness categories.
Stemming from environmental, genetic, and situational factors, chronic disease is a critical concern in modern medicine. Managing treatment and controlling symptoms is imperative to the longevity and quality of life of patients with such diseases. The Handbook of Research on Trends in the Diagnosis and Treatment of Chronic Conditions features current research on the diagnosis, monitoring, management, and treatment of recurring diseases such as diabetes, Parkinson's disease, autoimmune disorders, and others. This handbook is intended for practitioners and researchers across various disciplines including, but not limited to, biology, biomedical engineering, computer science, and information and communication technologies. Aimed at identifying new disease determinants and the way in which new technologies can contribute to improved health outcomes, this handbook covers a variety of topics, including wearable and mobile technologies, capillaroscopy imaging, diagnostic and monitoring methods, and disease prediction modeling, among others.
Structural Bioinformatics
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Author : Fouad Sabry
language : en
Publisher: One Billion Knowledgeable
Release Date : 2025-03-24
Structural Bioinformatics written by Fouad Sabry and has been published by One Billion Knowledgeable this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-24 with Science categories.
Unlock the power of Structural Bioinformatics—a crucial field in Molecular Biophysics that bridges computational analysis with biological insights. This book provides a comprehensive guide to understanding protein structures, molecular interactions, and computational tools that shape modern biology and drug discovery. Essential for students, researchers, and professionals, it offers a deep dive into this dynamic field. Chapters Brief Overview: 1: Structural bioinformatics – An introduction to computational methods for analyzing biomolecular structures. 2: Bioinformatics – Explores algorithms and databases that drive biological research and discoveries. 3: Protein – Examines protein structures, functions, and their role in biological processes. 4: Structural biology – Discusses techniques for determining molecular structures at atomic resolution. 5: Protein Data Bank – Highlights the importance of global repositories for protein structural data. 6: Protein structure prediction – Covers computational models for predicting unknown protein structures. 7: Structural alignment – Analyzes methods for comparing molecular conformations and evolutionary relationships. 8: Protein–protein interaction – Investigates how proteins interact and regulate cellular functions. 9: Macromolecular docking – Explains techniques for predicting molecular binding and interactions. 10: Internal Coordinate Mechanics – Introduces coordinatebased modeling of biomolecular movements. 11: Root mean square deviation of atomic positions – Evaluates structural similarities in biomolecules. 12: Biomolecular structure – Studies molecular architecture and its implications in biological systems. 13: Molecular biophysics – Integrates physics and biology to understand molecular behaviors. 14: Scoring functions for docking – Discusses methods for evaluating molecular docking accuracy. 15: Protein structure database – Explores various databases used in protein structural research. 16: Biological data visualization – Introduces graphical techniques for analyzing molecular structures. 17: Computer Atlas of Surface Topography of Proteins – Maps protein surface features for functional insights. 18: Structure validation – Reviews methods to ensure accuracy in molecular modeling. 19: ITASSER – Details a leading tool for protein structure prediction. 20: Molecular Operating Environment – Examines a software suite for molecular modeling. 21: Genomics – Connects genetic information with structural bioinformatics. This book is indispensable for those aiming to grasp the intricate details of biomolecular structures and their applications in medicine, biotechnology, and beyond. Whether you are a professional, student, or enthusiast, this book equips you with the knowledge and tools needed to excel in the evolving world of Molecular Biophysics.
Big Data In Omics And Imaging
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Author : Momiao Xiong
language : en
Publisher: CRC Press
Release Date : 2018-06-14
Big Data In Omics And Imaging written by Momiao Xiong and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-14 with Mathematics categories.
Big Data in Omics and Imaging: Integrated Analysis and Causal Inference addresses the recent development of integrated genomic, epigenomic and imaging data analysis and causal inference in big data era. Despite significant progress in dissecting the genetic architecture of complex diseases by genome-wide association studies (GWAS), genome-wide expression studies (GWES), and epigenome-wide association studies (EWAS), the overall contribution of the new identified genetic variants is small and a large fraction of genetic variants is still hidden. Understanding the etiology and causal chain of mechanism underlying complex diseases remains elusive. It is time to bring big data, machine learning and causal revolution to developing a new generation of genetic analysis for shifting the current paradigm of genetic analysis from shallow association analysis to deep causal inference and from genetic analysis alone to integrated omics and imaging data analysis for unraveling the mechanism of complex diseases. FEATURES Provides a natural extension and companion volume to Big Data in Omic and Imaging: Association Analysis, but can be read independently. Introduce causal inference theory to genomic, epigenomic and imaging data analysis Develop novel statistics for genome-wide causation studies and epigenome-wide causation studies. Bridge the gap between the traditional association analysis and modern causation analysis Use combinatorial optimization methods and various causal models as a general framework for inferring multilevel omic and image causal networks Present statistical methods and computational algorithms for searching causal paths from genetic variant to disease Develop causal machine learning methods integrating causal inference and machine learning Develop statistics for testing significant difference in directed edge, path, and graphs, and for assessing causal relationships between two networks The book is designed for graduate students and researchers in genomics, epigenomics, medical image, bioinformatics, and data science. Topics covered are: mathematical formulation of causal inference, information geometry for causal inference, topology group and Haar measure, additive noise models, distance correlation, multivariate causal inference and causal networks, dynamic causal networks, multivariate and functional structural equation models, mixed structural equation models, causal inference with confounders, integer programming, deep learning and differential equations for wearable computing, genetic analysis of function-valued traits, RNA-seq data analysis, causal networks for genetic methylation analysis, gene expression and methylation deconvolution, cell –specific causal networks, deep learning for image segmentation and image analysis, imaging and genomic data analysis, integrated multilevel causal genomic, epigenomic and imaging data analysis.
Hybrid Biomolecular Modeling
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Author : Slavica Jonic
language : en
Publisher: Frontiers Media SA
Release Date : 2019-01-24
Hybrid Biomolecular Modeling written by Slavica Jonic and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-24 with categories.
Models of biomolecular structure and dynamics are often obtained by combining simulation or prediction approaches (e.g., comparative modeling, Molecular Dynamics (MD) simulations, Normal Mode Analysis (NMA), etc.) with experimental approaches (e.g., Nuclear Magnetic Resonance (NMR), X-ray crystallography, Small-Angle X-ray Scattering (SAXS), Electron Microscopy (EM), etc.). Such hybrid modeling extends the capabilities of experimental techniques, by enriching structural information and facilitating dynamics studies of biomolecules. This eBook contains articles on methodological developments, applications, and challenges of hybrid biomolecular modeling that have been collected in the framework of the Frontiers Research Topic entitled “Hybrid Biomolecular Modeling”.