A Hybrid Algorithm for Determining Protein Structure

Authors:
Xiru Zhang
Affiliations:
-
Venue:
IEEE Expert: Intelligent Systems and Their Applications
Year:
1994

Citing 3
Cited 3

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ARIADNE: pattern-directed inference and hierarchical abstraction in protein structure recognition

Communications of the ACM
Identification of predictive sequence motifs limited by protein structure data base size

Nature

Protein Secondary Structure Prediction Using Data Mining Tool C5

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Towards a hybrid metabolic algorithm

WMC'06 Proceedings of the 7th international conference on Membrane Computing
Gauss-integral based representation of protein structure for predicting the fold class from the sequence

Mathematical and Computer Modelling: An International Journal

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Abstract

At Thinking Machines, my colleagues and I have developed a hybrid system combining a neural network, a statistical module, and a memory-based reasoner, each of which makes its own prediction. A combiner then blends these results to produce the final predictions. This hybrid system improves its ability to determine how amino acid sequences fold into 3D protein structures. It predicts secondary structures with 66.4% accuracy. Both the neural network and the combiner are multilayer perceptrons trained with the standard backpropagation algorithm; this article focuses on the other two components, and on how we trained the hybrid system and used it for prediction. I also discuss how future work in AI and other sciences might meet the challenge of the protein folding problem.