Machine Learning for Protein Subcellular Localization Prediction PDF Download
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Author: Shibiao Wan
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 1501501526
Category : Technology & Engineering
Languages : en
Pages : 213
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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: 1501501526
Category : Technology & Engineering
Languages : en
Pages : 213
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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: Daniela Cecconi
Publisher:
ISBN: 9781071616413
Category : Proteomics
Languages : en
Pages : 326
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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
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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: Shibiao Wan
Publisher:
ISBN: 9781501501517
Category : Machine learning
Languages : en
Pages :
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Book Description
Author: Mohammad Shamsul Arefin
Publisher: Springer Nature
ISBN: 9811666369
Category : Technology & Engineering
Languages : en
Pages : 784
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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: Thorsten Joachims
Publisher: Springer Science & Business Media
ISBN: 1461509076
Category : Computers
Languages : en
Pages : 218
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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: Jocelyn K. C. Rose
Publisher: CRC Press
ISBN: 9780849328114
Category : Science
Languages : en
Pages : 408
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Book Description
Enzymes, lignin, proteins, cellulose, pectin, kinase.
Author: G.D. Fasman
Publisher: Springer Science & Business Media
ISBN: 1461315719
Category : Science
Languages : en
Pages : 796
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Book Description
The prediction of the conformation of proteins has developed from an intellectual exercise into a serious practical endeavor that has great promise to yield new stable enzymes, products of pharmacological significance, and catalysts of great potential. With the application of predic tion gaining momentum in various fields, such as enzymology and immunology, it was deemed time that a volume be published to make available a thorough evaluation of present methods, for researchers in this field to expound fully the virtues of various algorithms, to open the field to a wider audience, and to offer the scientific public an opportunity to examine carefully its successes and failures. In this manner the practitioners of the art could better evaluate the tools and the output so that their expectations and applications could be more realistic. The editor has assembled chapters by many of the main contributors to this area and simultaneously placed their programs at three national resources so that they are readily available to those who wish to apply them to their personal interests. These algorithms, written by their originators, when utilized on pes or larger computers, can instantaneously take a primary amino acid sequence and produce a two-or three-dimensional artistic image that gives satisfaction to one's esthetic sensibilities and food for thought concerning the structure and function of proteins. It is in this spirit that this volume was envisaged.
Author: Andrzej Kloczkowski
Publisher: Humana
ISBN: 9781071641958
Category : Science
Languages : en
Pages : 0
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Book Description
This second edition volume expands on the previous edition with updates on the latest methods, resources, and studies concerning analysis and prediction of various structural and functional aspects of proteins and ncRNAs. The chapters in this book cover topics such as secondary structure characterization and prediction; the use and impact of AI (including AlphaFold, large language models, and deep neural networks) in the protein structure prediction field; methods and resources for the prediction of posttranslational modifications, residue-residue contacts, subcellular localization, intrinsic disorder, protein-ligand interactions, and protein aggregation; analysis of cryo-EM data; and analysis of noncoding RNAs in the context of human diseases. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions and surveys of the respective topics, list the necessary materials and methods, cover step-by-step instructions on how to use predictive tools and interpret their results, and provide tips on troubleshooting and avoiding known pitfalls. Cutting-edge and thorough, Prediction of Protein Secondary Structure, Second Edition is a valuable resource for anyone interested in understanding the dynamic and growing field of the protein structure prediction.
Author: IEEE Staff
Publisher:
ISBN: 9781665425551
Category :
Languages : en
Pages :
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Book Description
6th International Conference on Signal Processing, Computing and Control (ISPCC 2021) will be organized by Jaypee University of Information Technology, Waknaghat, India The aim of the ISPCC is to serve researchers, developers, educators working in the area of signal processing, computing, control, and their applications to present and future work as well as to exchange research ideas ISPCC 2021 invites authors to submit their original and unpublished work that demonstrates current research in all areas of signal processing, computing, control, and their applications The theme of the conference is Signal processing in Ubiquitous Systems However, ISPCC 2021 solicits original paper contributions in all of the related areas
