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Machine Learning In Bioinformatics Of Protein Sequences Algorithms Databases And Resources For Modern Protein Bioinformatics
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Book Synopsis Machine Learning In Bioinformatics Of Protein Sequences: Algorithms, Databases And Resources For Modern Protein Bioinformatics by : Lukasz Kurgan
Download or read book Machine Learning In Bioinformatics Of Protein Sequences: Algorithms, Databases And Resources For Modern Protein Bioinformatics written by Lukasz Kurgan and published by World Scientific. This book was released on 2022-12-06 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics by : Yi Pan
Download or read book Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics written by Yi Pan and published by John Wiley & Sons. This book was released on 2013-11-12 with total page 534 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics An in-depth look at the latest research, methods, and applications in the field of protein bioinformatics This book presents the latest developments in protein bioinformatics, introducing for the first time cutting-edge research results alongside novel algorithmic and AI methods for the analysis of protein data. In one complete, self-contained volume, Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics addresses key challenges facing both computer scientists and biologists, arming readers with tools and techniques for analyzing and interpreting protein data and solving a variety of biological problems. Featuring a collection of authoritative articles by leaders in the field, this work focuses on the analysis of protein sequences, structures, and interaction networks using both traditional algorithms and AI methods. It also examines, in great detail, data preparation, simulation, experiments, evaluation methods, and applications. Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics: Highlights protein analysis applications such as protein-related drug activity comparison Incorporates salient case studies illustrating how to apply the methods outlined in the book Tackles the complex relationship between proteins from a systems biology point of view Relates the topic to other emerging technologies such as data mining and visualization Includes many tables and illustrations demonstrating concepts and performance figures Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics is an essential reference for bioinformatics specialists in research and industry, and for anyone wishing to better understand the rich field of protein bioinformatics.
Book Synopsis Introduction to Protein Structure Prediction by : Huzefa Rangwala
Download or read book Introduction to Protein Structure Prediction written by Huzefa Rangwala and published by John Wiley & Sons. This book was released on 2011-03-16 with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt: A look at the methods and algorithms used to predict protein structure A thorough knowledge of the function and structure of proteins is critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this reference sheds light on the methods used for protein structure prediction and reveals the key applications of modeled structures. This indispensable book covers the applications of modeled protein structures and unravels the relationship between pure sequence information and three-dimensional structure, which continues to be one of the greatest challenges in molecular biology. With this resource, readers will find an all-encompassing examination of the problems, methods, tools, servers, databases, and applications of protein structure prediction and they will acquire unique insight into the future applications of the modeled protein structures. The book begins with a thorough introduction to the protein structure prediction problem and is divided into four themes: a background on structure prediction, the prediction of structural elements, tertiary structure prediction, and functional insights. Within those four sections, the following topics are covered: Databases and resources that are commonly used for protein structure prediction The structure prediction flagship assessment (CASP) and the protein structure initiative (PSI) Definitions of recurring substructures and the computational approaches used for solving sequence problems Difficulties with contact map prediction and how sophisticated machine learning methods can solve those problems Structure prediction methods that rely on homology modeling, threading, and fragment assembly Hybrid methods that achieve high-resolution protein structures Parts of the protein structure that may be conserved and used to interact with other biomolecules How the loop prediction problem can be used for refinement of the modeled structures The computational model that detects the differences between protein structure and its modeled mutant Whether working in the field of bioinformatics or molecular biology research or taking courses in protein modeling, readers will find the content in this book invaluable.
Book Synopsis Feature Representation and Learning Methods With Applications in Protein Secondary Structure by : Zhibin Lv
Download or read book Feature Representation and Learning Methods With Applications in Protein Secondary Structure written by Zhibin Lv and published by Frontiers Media SA. This book was released on 2021-10-25 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Ten Most Wanted Solutions in Protein Bioinformatics by : Anna Tramontano
Download or read book The Ten Most Wanted Solutions in Protein Bioinformatics written by Anna Tramontano and published by CRC Press. This book was released on 2005-05-24 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: Utilizing high speed computational methods to extrapolate to the rest of the protein universe, the knowledge accumulated on a subset of examples, protein bioinformatics seeks to accomplish what was impossible before its invention, namely the assignment of functions or functional hypotheses for all known proteins.The Ten Most Wanted Solutions in Pro
Book Synopsis Protein Bioinformatics by : Ingvar Eidhammer
Download or read book Protein Bioinformatics written by Ingvar Eidhammer and published by John Wiley & Sons. This book was released on 2004-02-13 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pairwise global alignment of sequences. Pairwise local alignment and database search. Statical analysis. Multiple global alignment and phylogenetic trees. Scoring matrices. Profiles. Sequence patterns. Structures and structure descriptions. Superposition and Dynamic programming. Geometric techniques. Clustering: Combining local similarities. Significance and assessment of structure comparisons. Multiple structure comparison. Protein structure classification. Structure prediction: Threading. Basics in mathematics, probability and algorithms. Introduction to molecular biology.
Book Synopsis Molecular Databases for Protein Sequences and Structure Studies by : John A.A. Sillince
Download or read book Molecular Databases for Protein Sequences and Structure Studies written by John A.A. Sillince and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: The amount of molecular information is too vast to be acquired without the use of computer-bases systems. The authors introduce students entering research in molecular biology and related fields into the efficient use of the numerous databases available. They show the broad scientific context of these databases and their latest developments. They also put the biological, chemical and computational aspects of structural information on biomolecules into perspective. The book is required reading for researchers and students who plan to use modern computer environment in their research.
Book Synopsis Handbook of Machine Learning Applications for Genomics by : Sanjiban Sekhar Roy
Download or read book Handbook of Machine Learning Applications for Genomics written by Sanjiban Sekhar Roy and published by Springer Nature. This book was released on 2022-06-23 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Currently, machine learning is playing a pivotal role in the progress of genomics. The applications of machine learning are helping all to understand the emerging trends and the future scope of genomics. This book provides comprehensive coverage of machine learning applications such as DNN, CNN, and RNN, for predicting the sequence of DNA and RNA binding proteins, expression of the gene, and splicing control. In addition, the book addresses the effect of multiomics data analysis of cancers using tensor decomposition, machine learning techniques for protein engineering, CNN applications on genomics, challenges of long noncoding RNAs in human disease diagnosis, and how machine learning can be used as a tool to shape the future of medicine. More importantly, it gives a comparative analysis and validates the outcomes of machine learning methods on genomic data to the functional laboratory tests or by formal clinical assessment. The topics of this book will cater interest to academicians, practitioners working in the field of functional genomics, and machine learning. Also, this book shall guide comprehensively the graduate, postgraduates, and Ph.D. scholars working in these fields.
Book Synopsis Protein Bioinformatics by : Frédérique Lisacek
Download or read book Protein Bioinformatics written by Frédérique Lisacek and published by Springer Nature. This book was released on with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Protein Structure Prediction by : Igor F. Tsigelny
Download or read book Protein Structure Prediction written by Igor F. Tsigelny and published by Internat'l University Line. This book was released on 2002 with total page 540 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Machine Learning Algorithms for Protein Structure Prediction by : Jianlin Cheng
Download or read book Machine Learning Algorithms for Protein Structure Prediction written by Jianlin Cheng and published by . This book was released on 2006 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Protein Bioinformatics by : M. Michael Gromiha
Download or read book Protein Bioinformatics written by M. Michael Gromiha and published by Academic Press. This book was released on 2011-04-21 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the most pressing tasks in biotechnology today is to unlock the function of each of the thousands of new genes identified every day. Scientists do this by analyzing and interpreting proteins, which are considered the task force of a gene. This single source reference covers all aspects of proteins, explaining fundamentals, synthesizing the latest literature, and demonstrating the most important bioinformatics tools available today for protein analysis, interpretation and prediction. Students and researchers of biotechnology, bioinformatics, proteomics, protein engineering, biophysics, computational biology, molecular modeling, and drug design will find this a ready reference for staying current and productive in this fast evolving interdisciplinary field. - Explains all aspects of proteins including sequence and structure analysis, prediction of protein structures, protein folding, protein stability, and protein interactions - Presents a cohesive and accessible overview of the field, using illustrations to explain key concepts and detailed exercises for students.
Book Synopsis Machine Learning for Protein Subcellular Localization Prediction by : Shibiao Wan
Download or read book Machine Learning for Protein Subcellular Localization Prediction written by Shibiao Wan and published by Walter de Gruyter GmbH & Co KG. This book was released on 2015-05-19 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensively covers protein subcellular localization from single-label prediction to multi-label prediction, and includes prediction strategies for virus, plant, and eukaryote species. Three machine learning tools are introduced to improve classification refinement, feature extraction, and dimensionality reduction.
Book Synopsis Practical Protein Bioinformatics by : Florencio Pazos
Download or read book Practical Protein Bioinformatics written by Florencio Pazos and published by Springer. This book was released on 2014-11-28 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes more than 60 web-accessible computational tools for protein analysis and is totally practical, with detailed explanations on how to use these tools and interpret their results and minimal mentions to their theoretical basis (only when that is required for making a better use of them). It covers a wide range of tools for dealing with different aspects of proteins, from their sequences, to their three-dimensional structures, and the biological networks they are immersed in. The selection of tools is based on the experience of the authors that lead a protein bioinformatics facility in a large research centre, with the additional constraint that the tools should be accessible through standard web browsers without requiring the local installation of specific software, command-line tools, etc. The web tools covered include those aimed to retrieve protein information, look for similar proteins, generate pair-wise and multiple sequence alignments of protein sequences, work with protein domains and motifs, study the phylogeny of a family of proteins, retrieve, manipulate and visualize protein three-dimensional structures, predict protein structural features as well as whole three-dimensional structures, extract biological information from protein structures, summarize large protein sets, study protein interaction and metabolic networks, etc. The book is associated to a dynamic web site that will reflect changes in the web addresses of the tools, updates of these, etc. It also contains QR codes that can be scanned with any device to direct its browser to the tool web site. This monograph will be most valuable for researchers in experimental labs without specific knowledge on bioinformatics or computing.
Book Synopsis MacHine-Learning Based Sequence Analysis, Bioinformatics and Nanopore Transduction Detection by : Stephen Winters-Hilt
Download or read book MacHine-Learning Based Sequence Analysis, Bioinformatics and Nanopore Transduction Detection written by Stephen Winters-Hilt and published by Lulu.com. This book was released on 2011-05-01 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is intended to be a simple and accessible book on machine learning methods and their application in computational genomics and nanopore transduction detection. This book has arisen from eight years of teaching one-semester courses on various machine-learning, cheminformatics, and bioinformatics topics. The book begins with a description of ad hoc signal acquisition methods and how to orient on signal processing problems with the standard tools from information theory and signal analysis. A general stochastic sequential analysis (SSA) signal processing architecture is then described that implements Hidden Markov Model (HMM) methods. Methods are then shown for classification and clustering using generalized Support Vector Machines, for use with the SSA Protocol, or independent of that approach. Optimization metaheuristics are used for tuning over algorithmic parameters throughout. Hardware implementations and short code examples of the various methods are also described.
Book Synopsis Protein Bioinformatics by : Cathy H. Wu
Download or read book Protein Bioinformatics written by Cathy H. Wu and published by . This book was released on 2017 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This volume introduces bioinformatics research methods for proteins, with special focus on protein post-translational modifications (PTMs) and networks. This book is organized into four parts and covers the basic framework and major resources for analysis of protein sequence, structure, and function; approaches and resources for analysis of protein PTMs, protein-protein interactions (PPIs) and protein networks, including tools for PPI prediction and approaches for the construction of PPI and PTM networks; and bioinformatics approaches in proteomics, including computational methods for mass spectrometry-based proteomics and integrative analysis for alternative splice isoforms, for functional discovery. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory or computational protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and thorough, Protein Bioinformatics: From Protein Modifications and Networks to Proteomics is a valuable resource for readers who wish to learn about state-of-the-art bioinformatics databases and tools, novel computational methods, and future trends in protein and proteomic data analysis in systems biology. This book is useful to researchers who work in the biotechnology and pharmaceutical industries, and in various academic departments, such as biological and medical sciences and computer sciences and engineering." -- OCLC.
Book Synopsis From Protein Structure to Function with Bioinformatics by : Daniel John Rigden
Download or read book From Protein Structure to Function with Bioinformatics written by Daniel John Rigden and published by Springer Science & Business Media. This book was released on 2008-12-11 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: Proteins lie at the heart of almost all biological processes and have an incredibly wide range of activities. Central to the function of all proteins is their ability to adopt, stably or sometimes transiently, structures that allow for interaction with other molecules. An understanding of the structure of a protein can therefore lead us to a much improved picture of its molecular function. This realisation has been a prime motivation of recent Structural Genomics projects, involving large-scale experimental determination of protein structures, often those of proteins about which little is known of function. These initiatives have, in turn, stimulated the massive development of novel methods for prediction of protein function from structure. Since model structures may also take advantage of new function prediction algorithms, the first part of the book deals with the various ways in which protein structures may be predicted or inferred, including specific treatment of membrane and intrinsically disordered proteins. A detailed consideration of current structure-based function prediction methodologies forms the second part of this book, which concludes with two chapters, focusing specifically on case studies, designed to illustrate the real-world application of these methods. With bang up-to-date texts from world experts, and abundant links to publicly available resources, this book will be invaluable to anyone who studies proteins and the endlessly fascinating relationship between their structure and function.
