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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.
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.
Based on ideas from Support Vector Machines (SVMs), Learning To Classify Text Using Support Vector Machines presents a new approach to generating text classifiers from examples. The approach combines high performance and efficiency with theoretical understanding and improved robustness. In particular, it is highly effective without greedy heuristic components. The SVM approach is computationally efficient in training and classification, and it comes with a learning theory that can guide real-world applications. Learning To Classify Text Using Support Vector Machines gives a complete and detailed description of the SVM approach to learning text classifiers, including training algorithms, transductive text classification, efficient performance estimation, and a statistical learning model of text classification. In addition, it includes an overview of the field of text classification, making it self-contained even for newcomers to the field. This book gives a concise introduction to SVMs for pattern recognition, and it includes a detailed description of how to formulate text-classification tasks for machine learning.
Machine learning techniques have been widely used for classification problems in computational biology. They require that the input must be a collection of fixedlength feature vectors. Since proteins are of varying lengths, there is a need for a means of representing protein sequences by a fixed-number of features. This thesis introduces three novel methods for this purpose: n-peptide compositions with reduced alphabets, pairwise similarity scores by maximal unique matches, and pairwise similarity scores by probabilistic suffix trees. New sequence representations described in the thesis are applied on three challenging problems of computational biology: remote homology detection, subcellular localization prediction, and solvent accessibility prediction, with some problem-specific modifications. Rigorous experiments are conducted on common benchmarking datasets, and a comparative analysis is performed between the new methods and the existing ones for each problem. On remote homology detection tests, all three methods achieve competitive accuracies with the state-of-the-art methods, while being much more efficient. A combination of new representations are used to devise a hybrid system, called PredLOC, for predicting subcellular localization of proteins and it is tested on two distinct eukaryotic datasets. To the best of author’s knowledge, the accuracy achieved by PredLOC is the highest one ever reported on those datasets. The maximal unique match method is resulted with only a slight improvement in solvent accessibility predictions.
Predicting the subcellular localization of a protein is a critical step in processes ranging from genome annotation to drug and vaccine target discovery. Previously developed methods for localization prediction in bacteria exhibit poor predictive performance and are not conducive to the high-throughput analysis required in this era of genome-scale biological analysis. We therefore developed PSORTb, a high-precision, high-throughput tool for the prediction of bacterial protein localization. PSORTb implements a multi-component approach to prediction, incorporating the detection of several sequence features known to influence subcellular localization. With a reported overall precision of 96%, it is the most precise method available and one of the most comprehensive methods capable of assigning a query protein to one or more of four Gram-positive or five Gram-negative localization sites. The PSORTb algorithm comprises a series of analytical steps, each step - or module - being an independent piece of software which scans the protein for the presence or absence of a particular sequence feature. Modules include: SCL-BLAST for homology-based detection, the HMMTOP transmembrane helix prediction tool, a signal peptide prediction tool, a series of frequent subsequence-based support vector machines, as well as motif and profile-matching modules. The modules return as output either a predicted localization site or - if the feature is not detected - a result of "unknown". The output is then integrated by a Bayesian network into a final prediction. Development of PSORTb also required the creation of PSORTdb, a database storing both known and predicted localization information for bacterial proteins. This is a valuable resource to both the localization prediction and microbial research communities, providing a source of training data for new predictive algorithms and acting as a discovery space. The release of PSORTb v.2.0 allowed us to carry out a number of analyses related to localization. We performed the first genome-wide computational and laboratory screen for Nterminal signal peptides in the opportunistic pathogen Pseudomonas aeruginosa, used PSORTb as a complement to laboratory-based high-throughput 2D gel studies of individual cellular compartments, and examined protein localization in a global context, revealing trends with implications for adaptive evolution in microbes.