Using knowledge-based neural networks to improve algorithms: Refining the Chou-Fasman algorithm for protein folding
نویسندگان
چکیده
منابع مشابه
Refining Algorithms with Knowledge-Based Neural Networks: Improving the Chou-Fasman Algorithm for Protein Folding*
We describe a method for using machine learning to refine algorithms represented as generalized finite-state automata. The knowledge in an automaton is translated into a corresponding artificial neural network, and then refined by applying backpropagation to a set of examples. Our technique for translating an automaton into a network extends the KBANN algorithm, a system that translates a set o...
متن کاملUsing Knowledge-Based Neural Networks to Improve Algorithms: Re ning the Chou-Fasman Algorithm for Protein Folding
We describe a method for using machine learning to re ne algorithms represented as generalized nite-state automata. The knowledge in an automaton is translated into an arti cial neural network, and then re ned with backpropagation on a set of examples. Our technique for translating an automaton into a network extends kbann, a system that translates a set of propositional rules into a correspond...
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The Chou-Fasman method has been a widely used method for protein secondary structure prediction. This method is based on knowledge about amino acid residues potential to form αhelical regions or β-sheet regions in proteins. Our main interest in this study was to examine the reliability of these Chou-Fasman parameters. We have calculated the Chou-Fasman parameters, with 95% confidence limits, ou...
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Neural networks have conventionally been used to predict protein secondary structure. However, they have not been used to improve the predictions of existing methods. This paper presents the design and implementation of a neural network to refine secondary structure prediction using information obtained from the DSSP and Chou-Fasman algorithms. The network was trained with input patterns consis...
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ژورنال
عنوان ژورنال: Machine Learning
سال: 1993
ISSN: 0885-6125,1573-0565
DOI: 10.1007/bf00993077