Background: Protein subcellular localization is crucial information to elucidate protein functions.
Owing to the need for large-scale genome analysis, computational method for efficiently predicting
protein subcellular localization is highly required. Although many previous works have been done
for this task, the problem is still challenging due to several reasons: the number of subcellular
locations in practice is large; distribution of protein in locations is imbalanced, that is the number
of protein in each location remarkably different; and there are many proteins located in multiple
locations. Thus it is necessary to explore new features and appropriate classification methods to
improve the prediction performance.
Results: In this paper we propose a new predicting method which combines two key ideas: 1)
Information of neighbour proteins in a probabilistic gene network is integrated to enrich the
prediction features. 2) Fuzzy k-NN, a classification method based on fuzzy set theory is applied to
predict protein locating in multiple sites. Experiment was conducted on a dataset consisting of 22
locations from Budding yeast proteins and significant improvement was observed.
Conclusion: Our results suggest that the neighbourhood information from functional gene
networks is predictive to subcellular localization. The proposed method thus can be integrated and
complementary to other available prediction methods.