Improving Protein Function Prediction using the Hierarchical Structure of the Gene Ontology
- Roman Eisner
- Brett Poulin, Computing Science
- Duane Szafron, UofA CS
- Paul Lu, Department of Computing Science
- Russ Greiner, Dept of Computing Science; PI of AICML
High performance and accurate protein function prediction is an important problem in molecular biology. Many contemporary ontologies, such as Gene Ontology (GO), have a hierarchical structure that can be exploited to improve the prediction accuracy, and lower the computational cost, of protein function prediction. We leverage the hierarchical structure of the ontology in two ways. First, we present a method of creating hierarchy-aware training sets for machine-learned classifiers and we show that, in the case of GO molecular function, it is the most accurate method compared to not considering the hierarchy during training. Second, we use the hierarchy to reduce the computational cost of classification. We also introduce a sound methodology for evaluating hierarchical classifiers using global cross-validation. Biologists often use sequence similarity (e.g. BLAST) to identify a Ânearest neighbor sequence and use the database annotations of this neighbor to predict protein function. In these cases, we use the hierarchy to improve accuracy by a small amount. When no similar sequences can be found (which is true for up to 40% of some common proteomes), our technique can improve accuracy by a more significant amount. Although this paper focuses on a specific important applicationÂprotein function prediction for the GO hierarchyÂthe techniques may be applied to any classification problem over a hierarchical ontology.
Citation
R. Eisner, B. Poulin, D. Szafron, P. Lu, R. Greiner. "Improving Protein Function Prediction using the Hierarchical Structure of the Gene Ontology". IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), San Diego, November 2005.| Keywords: | proteome analyst, GO hierarchy, machine learning, bioinformatics, medical informatics |
| Category: | In Conference |
BibTeX
@incollection{Eisner+al:CIBCB05,
author = {Roman Eisner and Brett Poulin and Duane Szafron and Paul Lu and
Russ Greiner},
title = {Improving Protein Function Prediction using the Hierarchical
Structure of the Gene Ontology},
booktitle = {IEEE Symposium on Computational Intelligence in Bioinformatics
and Computational Biology (CIBCB)},
year = 2005,
}Last Updated: April 28, 2012Submitted by Russ Greiner
