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Frequent Subsequence-Based Protein Localization
Author(s) -
Osmar R. Zaı̈ane,
Yang Wang,
Randy Goebel,
Gregory J. Taylor
Publication year - 2006
Publication title -
lecture notes in computer science
Language(s) - English
Resource type - Book series
SCImago Journal Rank - 0.249
H-Index - 400
eISSN - 1611-3349
pISSN - 0302-9743
ISBN - 3-540-33104-2
DOI - 10.1007/11691730_5
Subject(s) - subsequence , computer science , support vector machine , boosting (machine learning) , extracellular , artificial intelligence , computational biology , biochemistry , biology , mathematics , mathematical analysis , bounded function
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.

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