Neural Networks for Full-Scale Protein Sequence Classification: Sequence Encoding with Singular Value Decomposition |
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Authors: | Wu Cathy Berry Michael Shivakumar Sailaja McLarty Jerry |
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Affiliation: | (1) Department of Epidemiology/Biomathematics, The University of Texas Health Center at Tyler, 75710 Tyler, Texas;(2) Department of Computer Science, University of Tennessee, 37996-1301 Knoxville, Tennessee |
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Abstract: | A neural network classification method has been developed as an alternative approach to the search/organization problem of protein sequence databases. The neural networks used are three-layered, feed-forward, back-propagation networks. The protein sequences are encoded into neural input vectors by a hashing method that counts occurrences ofn-gram words. A new SVD (singular value decomposition) method, which compresses the long and sparsen-gram input vectors and captures semantics ofn-gram words, has improved the generalization capability of the network. A full-scale protein classification system has been implemented on a Cray supercomputer to classify unknown sequences into 3311 PIR (Protein Identification Resource) superfamilies/families at a speed of less than 0.05 CPU second per sequence. The sensitivity is close to 90% overall, and approaches 100% for large superfamilies. The system could be used to reduce the database search time and is being used to help organize the PIR protein sequence database. |
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Keywords: | neural networks database search protein classification sequence analysis superfamily singular value decomposition (SVD) |
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