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Specificity rule discovery in HIV-1 protease cleavage site analysis

Paper ID Volume ID Publish Year Pages File Format Full-Text
15535 1421 2008 8 PDF Available
Title
Specificity rule discovery in HIV-1 protease cleavage site analysis
Abstract

Several machine learning algorithms have recently been applied to modeling the specificity of HIV-1 protease. The problem is challenging because of the three issues as follows: (1) datasets with high dimensionality and small number of samples could misguide classification modeling and its interpretation; (2) symbolic interpretation is desirable because it provides us insight to the specificity in the form of human-understandable rules, and thus helps us to design effective HIV inhibitors; (3) the interpretation should take into account complexity or dependency between positions in sequences. Therefore, it is neccessary to investigate multivariate and feature-selective methods to model the specificity and to extract rules from the model.We have tested extensively various machine learning methods, and we have found that the combination of neural networks and decompositional approach can generate a set of effective rules. By validation to experimental results for the HIV-1 protease, the specificity rules outperform the ones generated by frequency-based, univariate or black-box methods.

Keywords
HIV-1 cleavage site prediction rule discovery
First Page Preview
Specificity rule discovery in HIV-1 protease cleavage site analysis
Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 32, Issue 1, February 2008, Pages 72–79
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering