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Author: Shibiao Wan Publisher: Walter de Gruyter GmbH & Co KG ISBN: 150150150X Category : Technology & Engineering Languages : en Pages : 209
Book Description
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.
Author: Shibiao Wan Publisher: Walter de Gruyter GmbH & Co KG ISBN: 150150150X Category : Technology & Engineering Languages : en Pages : 209
Book Description
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.
Author: Thorsten Joachims Publisher: Springer Science & Business Media ISBN: 1461509076 Category : Computers Languages : en Pages : 218
Book Description
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.
Author: Karen E. Nelson Publisher: Springer ISBN: 9781461446743 Category : Science Languages : en Pages : 1528
Book Description
Metagenomics has taken off as one of the major cutting-edge fields of research. The field has broad implications for human health and disease, animal production, and environmental health. Metagenomics has opened up a wealth of data, tools, technologies and applications that allow us to access the majority of organisms that we still cannot access in pure culture (an estimated 99% of microbial life). Numerous research groups are developing tools, approaches and applications to deal with this new field, as larger data sets from environments including the human body, the oceans and soils are being generated. See for example the human microbiome initiative (HMP; http://nihroadmap.nih.gov/hmp/) which has become a world-wide effort, and the Global Ocean Sampling (GOS) surveys; http://www.jcvi.org/cms/research/projects/gos/overview/). The number of publications as measured through PubMed that are focused on metagenomics continues to increase. The field of metagenomics continues to evolve with large common datasets available to the scientific community. A concerted effort is needed to collate all this information in a centralized place. By having all the information in an Encyclopedia form, we have an opportunity to gather seminal contributions from the leaders in the field, and at the same time provide this information to a significant number of junior and senior scientists. It is anticipated that the Encyclopedia will also be used by many other groups including, clinicians, undergraduate and graduate level students, as well as ethical and legal groups associated with or interested in the issues surrounding metagenome science.
Author: Mohammad Shamsul Arefin Publisher: Springer Nature ISBN: 9811666369 Category : Technology & Engineering Languages : en Pages : 784
Book Description
This book gathers a collection of high-quality peer-reviewed research papers presented at the International Conference on Big Data, IoT and Machine Learning (BIM 2021), held in Cox’s Bazar, Bangladesh, during 23–25 September 2021. The book covers research papers in the field of big data, IoT and machine learning. The book will be helpful for active researchers and practitioners in the field.
Author: Daniela Cecconi Publisher: ISBN: 9781071616413 Category : Proteomics Languages : en Pages : 326
Book Description
This thorough book collects methods and strategies to analyze proteomics data. It is intended to describe how data obtained by gel-based or gel-free proteomics approaches can be inspected, organized, and interpreted to extrapolate biological information. Organized into four sections, the volume explores strategies to analyze proteomics data obtained by gel-based approaches, different data analysis approaches for gel-free proteomics experiments, bioinformatic tools for the interpretation of proteomics data to obtain biological significant information, as well as methods to integrate proteomics data with other omics datasets including genomics, transcriptomics, metabolomics, and other types of data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detailed implementation advice that will ensure high quality results in the lab. Authoritative and practical, Proteomics Data Analysis serves as an ideal guide to introduce researchers, both experienced and novice, to new tools and approaches for data analysis to encourage the further study of proteomics.
Author: Yanqing Zhang Publisher: John Wiley & Sons ISBN: 0470397411 Category : Computers Languages : en Pages : 476
Book Description
An introduction to machine learning methods and their applications to problems in bioinformatics Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. From an internationally recognized panel of prominent researchers in the field, Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics. Coverage includes: feature selection for genomic and proteomic data mining; comparing variable selection methods in gene selection and classification of microarray data; fuzzy gene mining; sequence-based prediction of residue-level properties in proteins; probabilistic methods for long-range features in biosequences; and much more. Machine Learning in Bioinformatics is an indispensable resource for computer scientists, engineers, biologists, mathematicians, researchers, clinicians, physicians, and medical informaticists. It is also a valuable reference text for computer science, engineering, and biology courses at the upper undergraduate and graduate levels.
Author: Frank Y. Shih Publisher: John Wiley & Sons ISBN: 0470404612 Category : Technology & Engineering Languages : en Pages : 564
Book Description
A comprehensive guide to the essential principles of image processing and pattern recognition Techniques and applications in the areas of image processing and pattern recognition are growing at an unprecedented rate. Containing the latest state-of-the-art developments in the field, Image Processing and Pattern Recognition presents clear explanations of the fundamentals as well as the most recent applications. It explains the essential principles so readers will not only be able to easily implement the algorithms and techniques, but also lead themselves to discover new problems and applications. Unlike other books on the subject, this volume presents numerous fundamental and advanced image processing algorithms and pattern recognition techniques to illustrate the framework. Scores of graphs and examples, technical assistance, and practical tools illustrate the basic principles and help simplify the problems, allowing students as well as professionals to easily grasp even complicated theories. It also features unique coverage of the most interesting developments and updated techniques, such as image watermarking, digital steganography, document processing and classification, solar image processing and event classification, 3-D Euclidean distance transformation, shortest path planning, soft morphology, recursive morphology, regulated morphology, and sweep morphology. Additional topics include enhancement and segmentation techniques, active learning, feature extraction, neural networks, and fuzzy logic. Featuring supplemental materials for instructors and students, Image Processing and Pattern Recognition is designed for undergraduate seniors and graduate students, engineering and scientific researchers, and professionals who work in signal processing, image processing, pattern recognition, information security, document processing, multimedia systems, and solar physics.
Author: Lukasz Kurgan Publisher: World Scientific ISBN: 9811258597 Category : Science Languages : en Pages : 378
Book Description
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.
Author: Shibiao Wan
Author: Shibiao Wan
Author: Shibiao Wan
Author: Thorsten Joachims
Author: Karen E. Nelson
Author: Mohammad Shamsul Arefin
Author: Daniela Cecconi
Author: Yanqing Zhang
Author: Frank Y. Shih
Author: Lukasz Kurgan
Author: Quan Zou