Enzyme Function Classification Based on Borda Count Ranking Aggregation Method

Abstract

Prediction of enzyme functions is an important research topic due to their role in chemical reactions. In this paper, we propose a model for enzyme function classification that combines the outputs of different pairwise sequence alignments based on local sequence alignment. The output of each pairwise sequence alignment is represented by a ranked list, while the main idea of the proposed model is to combine all ranked lists into one ranked list. The candidate of the highest rank is then assigned as the function of the unknown sequence. Unbalanced and balanced datasets are used for evaluation, and the obtained results show that our approach yields good performance and that ranking aggregation achieves results better compared to all single sequence alignments.