Bioinformatics Advance Access originally published online on February 26, 2004
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Bioinformatics 20(11) © Oxford University Press 2004; all rights reserved.
Why neural networks should not be used for HIV-1 protease cleavage site prediction
Intelligent Systems Laboratory, School of Information Science, Computer and Electrical Engineering, Halmstad University, Box 823, 301 18 Sweden
Received on August 25, 2003; revised on December 26, 2003; accepted on January 19, 2004
Advance Access Publication February 26, 2004
Summary: Several papers have been published where nonlinear machine learning algorithms, e.g. artificial neural networks, support vector machines and decision trees, have been used to model the specificity of the HIV-1 protease and extract specificity rules. We show that the dataset used in these studies is linearly separable and that it is a misuse of nonlinear classifiers to apply them to this problem. The best solution on this dataset is achieved using a linear classifier like the simple perceptron or the linear support vector machine, and it is straightforward to extract rules from these linear models. We identify key residues in peptides that are efficiently cleaved by the HIV-1 protease and list the most prominent rules, relating them to experimental results for the HIV-1 protease.
Motivation: Understanding HIV-1 protease specificity is important when designing HIV inhibitors and several different machine learning algorithms have been applied to the problem. However, little progress has been made in understanding the specificity because nonlinear and overly complex models have been used.
Results: We show that the problem is much easier than what has previously been reported and that linear classifiers like the simple perceptron or linear support vector machines are at least as good predictors as nonlinear algorithms. We also show how sets of specificity rules can be generated from the resulting linear classifiers.
Availability: The datasets used are available at http://www.hh.se/staff/bioinf/
Contact: denni{at}ide.hh.se
* To whom correspondence should be addressed.
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