Yifeng Liu
Osmar R. Zaïane
ABSTRACT
Protein subcellular localization prediction is the problem of predicting where a protein functions within a living cell. In this paper, we apply associative classifications (CMAR and CPAR) and multi-class Support Vector Machines to tackle the problem of protein subcellular localization prediction. We use classification feature sources generated from a protein's SwissProt annotation record. We visualize the applied classification rules in an explain graph for domain experts to interpret. We compare the performance of our approaches to those of Proteome Analyst 3.0, using the same set of classification features; we find that all three classification algorithms outperform Proteome Analyst. Multi-class SVM achieves overall F-measures [0.934 ~ 0.991], while CPAR and CMAR achieve overall F-measures [0.922 ~ 0.989] and [0.880 ~ 0.989], respectively. Our result shows that despite multi-class SVM is still the most accurate prediction algorithm with overall F-measures, CPAR and CMAR achieve very similar accuracy. In most cases, CPAR outperforms CMAR, especially when the feature space is large. Our result indicates that associative classification algorithms, especially CPAR, is a good alternative to SVM with similar accuracy but much better transparency in classification models.
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Index Terms
- Protein subcellular localization prediction with associative classification and multi-class SVM
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