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The importance of larger data sets for protein secondary structure prediction with neural networks
Author(s) -
Chandonia JohnMarc,
Karplus Martin
Publication year - 1996
Publication title -
protein science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.353
H-Index - 175
eISSN - 1469-896X
pISSN - 0961-8368
DOI - 10.1002/pro.5560050422
Subject(s) - artificial neural network , conjugate gradient method , gradient descent , protein secondary structure , sequence (biology) , computer science , algorithm , class (philosophy) , data mining , artificial intelligence , chemistry , biochemistry
A neural network algorithm is applied to secondary structure and structural class prediction for a database of 318 nonhomologous protein chains. Significant improvement in accuracy is obtained as compared with performance on smaller databases. A systematic study of the effects of network topology shows that, for the larger database, better results are obtained with more units in the hidden layer. In a 32‐fold cross validated test, secondary structure prediction accuracy is 67.0%, relative to 62.6% obtained previously, without any evolutionary information on the sequence. Introduction of sequence profiles increases this value to 72.9%, suggesting that the two types of information are essentially independent. Tertiary structural class is predicted with 80.2% accuracy, relative to 73.9% obtained previously. The use of a larger database is facilitated by the introduction of a scaled conjugate gradient algorithm for optimizing the neural network. This algorithm is about 10–20 times as fast as the standard steepest descent algorithm.