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.
Table of contents
- Cover
- Also of Interest
- Title Page
- Copyright Page
- Preface
- Contents
- List of Abbreviations
- 1 Introduction
- 2 Overview of subcellular localization prediction
- 3 Legitimacy of using gene ontology information
- 4 Single-location protein subcellular localization
- 5 From single- to multi-location
- 6 Mining deeper on GO for protein subcellular localization
- 7 Ensemble random projection for large-scale predictions
- 8 Experimental setup
- 9 Results and analysis
- 10 Properties of the proposed predictors
- 11 Conclusions and future directions
- A Webservers for protein subcellular localization
- B Support vector machines
- C Proof of no bias in LOOCV
- D Derivatives for penalized logistic regression
- Endnotes
- Bibliography
- Index
Product information
- Title: Machine Learning for Protein Subcellular Localization Prediction
- Author(s):
- Release date: May 2015
- Publisher(s): De Gruyter
- ISBN: 9781501501524
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