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48 lines
1.9 KiB
Plaintext
Executable File
48 lines
1.9 KiB
Plaintext
Executable File
NAME: Secondary Structure of Globular Proteins
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SUMMARY: This is a data set used by Ning Qian and Terry Sejnowski in their
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study using a neural net to predict the secondary structure of certain
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globular proteins [1]. The idea is to take a linear sequence of amino
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acids and to predict, for each of these amino acids, what secondary
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structure it is a part of within the protein. There are three choices:
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alpha-helix, beta-sheet, and random-coil. The data set contains both a
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large set of training data and a distinct set of data that can be used for
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testing the resulting network. Qian and Sejnowski use a Nettalk-like
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approach and report an accuracy of 64.3% on the test set, and they
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speculate that this is about the best that can be done using only local
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context.
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SOURCE: The data set was contributed to the benchmark collection by Terry
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Sejnowski, now at the Salk Institute and the University of California at
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San Deigo. The data set was developed in collaboration with Ning Qian of
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Johns-Hopkins University.
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COPYRIGHT STATUS: The data files carry the following copyright notice:
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Copyright (C) 1988 by Terrence J. Sejnowski. Permission is hereby given to
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use the included data for non-commercial research purposes. Contact The
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Johns Hopkins University, Cognitive Science Center, Baltimore MD, USA for
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information on commercial use.
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MAINTAINER: Scott E. Fahlman
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PROBLEM DESCRIPTION:
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<< Summary not yet written. See [1] for details. >>
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METHODOLOGY:
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This data set can be used in a number of different ways to test learning
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speed, quality of ultimate learning, ability to generalize, or combinations
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of these factors.
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RESULTS:
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<< Summary not yet written. See [1] for details. >>
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REFERENCES:
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1. Ning Qian and Terrnece J. Sejnowski (1988), "Predicting the Secondary Structure
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of Globular Proteins Using Neural Network Models" in Journal of Molecular
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Biology 202, 865-884. Academic Press.
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