Machine Learning for Protein Subcellular Localization Prediction

Machine Learning for Protein Subcellular Localization Prediction
Author: Shibiao Wan
Publisher: Walter de Gruyter GmbH & Co KG
Total Pages: 213
Release: 2015-05-19
Genre: Technology & Engineering
ISBN: 1501501526
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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.


Machine Learning for Protein Subcellular Localization Prediction
Language: en
Pages: 213

Machine Learning for Protein Subcellular Localization Prediction

Authors: Shibiao Wan
Categories: Technology & Engineering
Type: BOOK - Published: 2015-05-19 - Publisher: Walter de Gruyter GmbH & Co KG

GET EBOOK

Comprehensively covers protein subcellular localization from single-label prediction to multi-label prediction, and includes prediction strategies for virus, pl
Machine Learning for Protein Subcellular Localization Prediction
Language: en
Pages:

Machine Learning for Protein Subcellular Localization Prediction

Authors: Shibiao Wan
Categories: Machine learning
Type: BOOK - Published: 2015 - Publisher:

GET EBOOK

Proceedings of the 4th Asia-Pacific Bioinformatics Conference
Language: en
Pages: 388

Proceedings of the 4th Asia-Pacific Bioinformatics Conference

Authors: Tao Jiang
Categories: Computers
Type: BOOK - Published: 2006 - Publisher: Advances in Bioinformatics and

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High-throughput sequencing and functional genomics technologies have given us a draft human genome sequence and have enabled large-scale genotyping and gene exp
Predicting Protein Sub-cellular Localization from Homologs Using Machine Learning Algorithms
Language: en
Pages: 118

Predicting Protein Sub-cellular Localization from Homologs Using Machine Learning Algorithms

Authors: Zhiyong Lu
Categories: Proteins
Type: BOOK - Published: 2003 - Publisher:

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Proteomics Data Analysis
Language: en
Pages: 326

Proteomics Data Analysis

Authors: Daniela Cecconi
Categories: Proteomics
Type: BOOK - Published: 2021 - Publisher:

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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