Abstract
Extracellular plant proteins are involved in numerous pro- cesses including nutrient acquisition, communication with other soil organisms, protection from pathogens, and resistance to disease and toxic metals. Insofar as these proteins are strategically positioned to play a role in resistance to environmental stress, biologists are interested in proteomic tools in analyzing extracellular proteins. In this paper, we present three methods using frequent subsequences of amino acids: one based on support vector machines (SVM), one based on boosting and FSP, a new frequent subsequence pattern method. We test our methods on a plant dataset and the experimental results show that our methods perform better than the existing approaches based on amino acid composition.
Research funded in part by the Alberta Ingenuity Funds and NSERC Canada.
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Zaïane, O.R., Wang, Y., Goebel, R., Taylor, G. (2006). Frequent Subsequence-Based Protein Localization. In: Li, J., Yang, Q., Tan, AH. (eds) Data Mining for Biomedical Applications. BioDM 2006. Lecture Notes in Computer Science(), vol 3916. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11691730_5
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DOI: https://doi.org/10.1007/11691730_5
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