Bayesian Sequence Learning for Predicting Protein Cleavage Points | Zendy

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Bayesian Sequence Learning for Predicting Protein Cleavage Points

Author(s) -

Michael Mayo

Publication year - 2005

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 - 3-540-26076-5

DOI - 10.1007/11430919_25

Subject(s) - computer science , artificial intelligence , artificial neural network , sequence (biology) , bayesian probability , data mining , machine learning , bayesian network , state (computer science) , pattern recognition (psychology) , algorithm , genetics , biology

A challenging problem in data mining is the application of efficient techniques to automatically annotate the vast databases of biological sequence data. This paper describes one such application in this area, to the prediction of the position of signal peptide cleavage points along protein sequences. It is shown that the method, based on Bayesian statistics, is comparable in terms of accuracy to the existing state-of-the-art neural network techniques while providing explanatory information for its predictions

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