Publication Type
Report
Version
publishedVersion
Publication Date
6-2010
Abstract
Subcellular localization is a key functional characteristic of proteins. Optimally combining available information is one of the key challenges in today's knowledge-based subcellular localization prediction approaches. This study explores machine learning approaches for the prediction of protein subcellular localization that use resources concerning Gene Ontology and secondary structures. Using the spectrum kernel for feature representation of amino acid sequences and secondary structures, we explore an SVM-based learning method that classifies six subcellular localization sites: endoplasmic reticulum, extracellular, Golgi, membrane, mitochondria, and nucleus.
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Research Areas
Data Science and Engineering
Publication
Technical Report University of Minnesota
Issue
TR 10-014
First Page
1
Last Page
15
Publisher
University of Minnesota, Department of Computer Science and Engineering
City or Country
Minneapolis, MN
Citation
SHIM, Kyong Jin.
Prediction of Protein Subcellular Localization: A Machine Learning Approach. (2010). Technical Report University of Minnesota. 1-15.
Available at: https://ink.library.smu.edu.sg/sis_research/1526
Copyright Owner and License
Author
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://www.cs.umn.edu/sites/cs.umn.edu/files/tech_reports/10-014.pdf
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons