Machine Learning for Protein Subcellular Localization Prediction - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning for Protein Subcellular Localization Prediction write by Shibiao Wan. This book was released on 2015-05-19. Machine Learning for Protein Subcellular Localization Prediction available in PDF, EPUB and Kindle. 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.
Learning to Classify Text Using Support Vector Machines
Learning to Classify Text Using Support Vector Machines - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Learning to Classify Text Using Support Vector Machines write by Thorsten Joachims. This book was released on 2002-04-30. Learning to Classify Text Using Support Vector Machines available in PDF, EPUB and Kindle. 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.
Proteomics Data Analysis
Proteomics Data Analysis - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Proteomics Data Analysis write by Daniela Cecconi. This book was released on 2021. Proteomics Data Analysis available in PDF, EPUB and Kindle. 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.
Machine Learning for Protein Subcellular Localization Prediction
Machine Learning for Protein Subcellular Localization Prediction - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning for Protein Subcellular Localization Prediction write by Shibiao Wan. This book was released on 2015. Machine Learning for Protein Subcellular Localization Prediction available in PDF, EPUB and Kindle.
Machine Learning in Bioinformatics
Machine Learning in Bioinformatics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning in Bioinformatics write by Yanqing Zhang. This book was released on 2009-02-23. Machine Learning in Bioinformatics available in PDF, EPUB and Kindle. 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.
