Predicting Sub-cellular Localization of Proteins using Machine-Learned Classifiers

• Author(s) / Creator(s)

  • Greiner, Russell
  • Wishart, David
  • Eisner, Roman
  • Lu, Z.
  • Lu, Paul
  • Macdonell, Cam
  • Poulin, B.
  • Szafron, Duane
  • Anvik, J.

• Technical report TR03-14. Identifying the destination or localization of proteins is key to understanding their function and facilitating their purification. A number of existing computational prediction methods are based on sequence analysis. However, these methods are limited in scope, accuracy and most particularly breadth of coverage. Rather than using sequence information alone, we have explored the use of database text annotations from homologs and machine learning to substantially improve the prediction of subcellular location. Results: We have constructed five machine-learning classifiers for predicting subcellular localization of proteins from animals, plants, fungi, Gram-negative bacteria and Gram-positive bacteria, which are 81% accurate for fungi and 92% to 94% accurate for the other four categories. These are the most accurate subcellular predictors across the widest set of organisms ever published. Our predictors are part of the Proteome Analyst (PA) web-service. Availability http://www.cs.ualberta.ca/~bioinfo/PA/Sub http://www.cs.ualberta.ca/~bioinfo/PA Supplementary Information http://www.cs.ualberta.ca/~bioinfo/PA/Subcellular Contact bioinfo@cs.ualberta.ca | TRID-ID TR03-14

• Date created

  2003

• Subjects / Keywords

  • Machine Learning
  • Sub-cellular localization
  • Bioinformatics
  • Protein annotation
  • Machine learning
  • Naive Bayes classifier

• Type of Item

  Report

• DOI

  https://doi.org/10.7939/R3HH6CC1X

• License

  Attribution 3.0 International