This work presents the latest development in the field of computational intelligence to advance Big Data and Cloud Computing concerning applications in medical diagnosis. As forum for academia and professionals it covers state-of-the-art research challenges and issues in the digital information & knowledge management and the concerns along with the solutions adopted in these fields.
Автор: D. Frishman Название: Structural Bioinformatics of Membrane Proteins ISBN: 3709116805 ISBN-13(EAN): 9783709116807 Издательство: Springer Рейтинг: Цена: 21661.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: With a focus on membrane proteins from the perspective of bioinformatics, this book covers a broad spectrum of topics in evolution, structure, function and bioinformatics of membrane proteins focusing on the most recent experimental results.
Автор: Florencio Pazos; M?nica Chagoyen Название: Practical Protein Bioinformatics ISBN: 3319127268 ISBN-13(EAN): 9783319127262 Издательство: Springer Рейтинг: Цена: 19564.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
Автор: Cathy H. Wu; Cecilia N. Arighi; Karen E. Ross Название: Protein Bioinformatics ISBN: 1493967819 ISBN-13(EAN): 9781493967810 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This volume introduces bioinformatics research methods for proteins, with special focus on protein post-translational modifications (PTMs) and networks.
Автор: Krishna Mohan Poluri; Khushboo Gulati Название: Protein Engineering Techniques ISBN: 9811027315 ISBN-13(EAN): 9789811027314 Издательство: Springer Рейтинг: Цена: 8384.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This brief provides a broad overview of protein-engineering research, offering a glimpse of the most common experimental methods. It also presents various computational programs with applications that are widely used in directed evolution, computational and de novo protein design. Further, it sheds light on the advantages and pitfalls of existing methodologies and future perspectives of protein engineering techniques.
Описание: This book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages. He argues that this is the case because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology. This book starts from very basic linear algebra and reaches the cutting edge methodologies applied to difficult situations when there are many features (variables) while only small number of samples are available. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tenor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics.
Allows readers to analyze data sets with small samples and many features;Provides a fast algorithm, based upon linear algebra, to analyze big data;Includes several applications to multi-view data analyses, with a focus on bioinformatics.
Описание: This work focuses on proteins and their structures, protein structure similarity searching at main representation levels and various techniques that can be used to accelerate similarity searches.
Автор: Gabriel Waksman Название: Proteomics and Protein-Protein Interactions ISBN: 1489996117 ISBN-13(EAN): 9781489996114 Издательство: Springer Рейтинг: Цена: 28732.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Gabriel Waksman Institute of Structural Molecular Biology, Birkbeck and University College London, Malet Street, London WC1E 7HX, United Kingdom Address for correspondence: Professor Gabriel Waksman Institute of Structural Molecular Biology Birkbeck and University College London Malet Street London WC1E 7H United Kingdom Email: g.
Автор: Florencio Pazos; M?nica Chagoyen Название: Practical Protein Bioinformatics ISBN: 3319381849 ISBN-13(EAN): 9783319381848 Издательство: Springer Рейтинг: Цена: 14365.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
INTRODUCTION
1. SEQUENCES - INTRODUCTION - REPRESENTING PROTEIN SEQUENCES IN THE COMPUTER Sequence file formats Sequence format conversion tools MAIN PROTEIN SEQUENCE DATABASES BASIC SEQUENCE-BASED CHARACTERISTICS COMPARE TWO PROTEIN SEQUENCES Types of pair-wise sequence alignments FINDING SIMILAR SEQUENCES IN A DATABASE (BASIC) Which sequence database to search? BLAST COMPARE MORE THAN TWO SEQUENCES Multiple Sequence Alignments: Formats and Conversion Alignment editing and representation Summarizing MSAs FINDING SIMILAR SEQUENCES IN A DATABASE (ADVANCED) Sequence profiles Iterative profile construction HMM profile search against a sequence database HMM profile search against a profile database PROTEIN MOTIFS, DOMAINS AND FAMILIES BASIC PHYLOGENY
2. STRUCTURES INTRODUCTION Storing protein structures - The PDB file format MAIN PROTEIN STRUCTURE DATABASES Classifications of structural domains STRUCTURE MANIPULATION, VISUALIZATION AND COMPARISON Structure Manipulation and Visualization Structure Comparison PREDICTION OF 1D STRUCTURAL FEATURES Secondary structure and solvent accessibility Transmembrane segments Coiled-coils Disordered regions Protein sorting signals PREDICTING PROTEIN 3D STRUCTURE Template-based (homology-based approaches) Template-based (fragment-based approaches) Model quality checks ANALYSIS OF PROTEIN STRUCTURE Mapping conservation Protein and ligand contacts Surface clefts, binding pockets, tunnels and internal cavities
3. SYSTEMS INTRODUCTION Protein/gene functional annotations ID conversions ANNOTATION ENRICHMENT ANALYSIS OF LARGE PROTEINS SETS PROTEIN INTERACTION NETWORKS METABOLIC NETWORKS Retrieve the metabolic-related information associated to a protein of interest Map a large set of proteins in the metabolome OTHER BIOLOGICAL NETWORKS
Описание: Complexes of physically interacting proteins constitute fundamental functional units that drive almost all biological processes within cells. A faithful reconstruction of the entire set of protein complexes (the "complexosome") is therefore important not only to understand the composition of complexes but also the higher level functional organization within cells. Advances over the last several years, particularly through the use of high-throughput proteomics techniques, have made it possible to map substantial fractions of protein interactions (the "interactomes") from model organisms including Arabidopsis thaliana (a flowering plant), Caenorhabditis elegans (a nematode), Drosophila melanogaster (fruit fly), and Saccharomyces cerevisiae (budding yeast). These interaction datasets have enabled systematic inquiry into the identification and study of protein complexes from organisms. Computational methods have played a significant role in this context, by contributing accurate, efficient, and exhaustive ways to analyze the enormous amounts of data. These methods have helped to compensate for some of the limitations in experimental datasets including the presence of biological and technical noise and the relative paucity of credible interactions.In this book, we systematically walk through computational methods devised to date (approximately between 2000 and 2016) for identifying protein complexes from the network of protein interactions (the protein-protein interaction (PPI) network). We present a detailed taxonomy of these methods, and comprehensively evaluate them for protein complex identification across a variety of scenarios including the absence of many true interactions and the presence of false-positive interactions (noise) in PPI networks. Based on this evaluation, we highlight challenges faced by the methods, for instance in identifying sparse, sub-, or small complexes and in discerning overlapping complexes, and reveal how a combination of strategies is necessary to accurately reconstruct the entire complexosome.
Описание: Complexes of physically interacting proteins constitute fundamental functional units that drive almost all biological processes within cells. A faithful reconstruction of the entire set of protein complexes (the "complexosome") is therefore important not only to understand the composition of complexes but also the higher level functional organization within cells. Advances over the last several years, particularly through the use of high-throughput proteomics techniques, have made it possible to map substantial fractions of protein interactions (the "interactomes") from model organisms including Arabidopsis thaliana (a flowering plant), Caenorhabditis elegans (a nematode), Drosophila melanogaster (fruit fly), and Saccharomyces cerevisiae (budding yeast). These interaction datasets have enabled systematic inquiry into the identification and study of protein complexes from organisms. Computational methods have played a significant role in this context, by contributing accurate, efficient, and exhaustive ways to analyze the enormous amounts of data. These methods have helped to compensate for some of the limitations in experimental datasets including the presence of biological and technical noise and the relative paucity of credible interactions.In this book, we systematically walk through computational methods devised to date (approximately between 2000 and 2016) for identifying protein complexes from the network of protein interactions (the protein-protein interaction (PPI) network). We present a detailed taxonomy of these methods, and comprehensively evaluate them for protein complex identification across a variety of scenarios including the absence of many true interactions and the presence of false-positive interactions (noise) in PPI networks. Based on this evaluation, we highlight challenges faced by the methods, for instance in identifying sparse, sub-, or small complexes and in discerning overlapping complexes, and reveal how a combination of strategies is necessary to accurately reconstruct the entire complexosome.
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