Abstract
Proteins are classified into families based on structural or functional similarities. Artificial intelligence methods such as Hidden Markov Models, Neural Networks and Fuzzy Logic have been used individually in the field of bioinformatics for tasks such as protein classification and microarray data analysis. We integrate these three methods into a protein classification system for the purpose of drug target identification. Through integration, the strengths of each method can be harnessed as one, and their weaknesses compensated. Artificial intelligence methods are more flexible than traditional multiple alignment methods, and hence, offers greater problem-solving potential.
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© 2005 Springer-Verlag Berlin Heidelberg
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Keat, M.C.W., Abdullah, R., Salam, R.A. (2005). Parallel Artificial Intelligence Hybrid Framework for Protein Classification. In: Konagaya, A., Satou, K. (eds) Grid Computing in Life Science. LSGRID 2004. Lecture Notes in Computer Science(), vol 3370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32251-1_9
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DOI: https://doi.org/10.1007/978-3-540-32251-1_9
Publisher Name: Springer, Berlin, Heidelberg
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