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ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
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PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
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CPM '92 Proceedings of the Third Annual Symposium on Combinatorial Pattern Matching
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IEEE Transactions on Knowledge and Data Engineering
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ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
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Mining minimal distinguishing subsequence patterns with gap constraints
Knowledge and Information Systems
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MSR '07 Proceedings of the Fourth International Workshop on Mining Software Repositories
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IEEE Transactions on Knowledge and Data Engineering
g-MARS: Protein Classification Using Gapped Markov Chains and Support Vector Machines
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
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Data Mining and Knowledge Discovery
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IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
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Artificial Intelligence in Medicine
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Neurocomputing
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DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
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WAIM'11 Proceedings of the 12th international conference on Web-age information management
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BioDM'06 Proceedings of the 2006 international conference on Data Mining for Biomedical Applications
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IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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ACM Transactions on Knowledge Discovery from Data (TKDD)
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HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
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Information Processing and Management: an International Journal
CSSF-trie structure to mine constraint sequential patterns from progressive database
International Journal of Knowledge Engineering and Data Mining
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A number of medically important disease-causing bacteria (collectively called Gram-negative bacteria) are noted for the extra "outer" membrane that surrounds their cell. Proteins resident in this membrane (outer membrane proteins, or OMPs) are of primary research interest for antibiotic and vaccine drug design as they are on the surface of the bacteria and so are the most accessible targets to develop new drugs against. With the development of genome sequencing technology and bioinformatics, biologists can now deduce all the proteins that are likely produced in a given bacteria and have attempted to classify where proteins are located in a bacterial cell. However such protein localization programs are currently least accurate when predicting OMPs, and so there is a current need for the development of a better OMP classifier. Data mining research suggests that the use of frequent patterns has good performance in aiding the development of accurate and efficient classification algorithms. In this paper, we present two methods to identify OMPs based on frequent subsequences and test them on all Gram-negative bacterial proteins whose localizations have been determined by biological experiments. One classifier follows an association rule approach, while the other is based on support vector machines (SVMs). We compare the proposed methods with the state-of-the-art methods in the biological domain. The results demonstrate that our methods are better both in terms of accurately identifying OMPs and providing biological insights that increase our understanding of the structures and functions of these important proteins.