Prediction of Protein Subcellular Localization: A Machine Learning Approach
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.
Databases and Information Systems
Data Management and Analytics
Technical Report University of Minnesota Department of Computer Science and Engineering
University of Minnesota
City or Country
SHIM, Kyong Jin.
Prediction of Protein Subcellular Localization: A Machine Learning Approach. (2010). Technical Report University of Minnesota Department of Computer Science and Engineering. 1-15. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1526