NAME: Secondary Structure of Globular Proteins 

SUMMARY: This is a data set used by Ning Qian and Terry Sejnowski in their
study using a neural net to predict the secondary structure of certain
globular proteins [1].  The idea is to take a linear sequence of amino
acids and to predict, for each of these amino acids, what secondary
structure it is a part of within the protein.  There are three choices:
alpha-helix, beta-sheet, and random-coil.  The data set contains both a
large set of training data and a distinct set of data that can be used for
testing the resulting network.  Qian and Sejnowski use a Nettalk-like
approach and report an accuracy of 64.3% on the test set, and they
speculate that this is about the best that can be done using only local
context.

SOURCE: The data set was contributed to the benchmark collection by Terry
Sejnowski, now at the Salk Institute and the University of California at
San Deigo.  The data set was developed in collaboration with Ning Qian of
Johns-Hopkins University.

COPYRIGHT STATUS: The data files carry the following copyright notice:

Copyright (C) 1988 by Terrence J. Sejnowski.  Permission is hereby given to
use the included data for non-commercial research purposes.  Contact The
Johns Hopkins University, Cognitive Science Center, Baltimore MD, USA for
information on commercial use.

MAINTAINER: neural-bench@cs.cmu.edu

PROBLEM DESCRIPTION:  

The problem is specified by the accompanying data file, "protein.data".  This
data file is in the standard CMU benchmark format.

METHODOLOGY: 

This data set can be used in a number of different ways to test learning
speed, quality of ultimate learning, ability to generalize, or combinations
of these factors.

RESULTS: 

In [1], Qian and Sejnowski report a success rate of 64.3% using cascaded
backprop networks.  In standard two-layered backprop models, results 
varied according to the number of hidden units present in the network.  The
results are as follows:

	Hidden units			Success Rate (%)
	~~~~~~~~~~~~			~~~~~~~~~~~~~~~~
	     0				      62.5
	     3				      62.5
	     5				      61.6
	     7				      62.2
	    10				      61.5
	    15				      62.6
	    20				      62.3
	    30				      62.5
	    40				      62.7
	    60				      61.4


REFERENCES: 

1. Ning Qian and Terrnece J. Sejnowski (1988), "Predicting the Secondary Structure
of Globular Proteins Using Neural Network Models" in Journal of Molecular
Biology 202, 865-884.  Academic Press.