- author = "Zheng Rong Yang and Andrew R. Dalby and Jing Qiu",
- title = "Mining {HIV} protease cleavage data using genetic programming with a sum-product function",
- journal = "Bioinformatics",
- year = "2004",
- volume = "20",
- number = "18",
- pages = "3398--3405",
- keywords = "genetic algorithms, genetic programming, HIV protease, enzyme, min-max scoring function, sum-production scoring function",
- ISSN = "1367--4803",
- language = "en",
- oai = "oai:CiteSeerX.psu:10.1.1.612.3378",
-
URL = "
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.612.3378",
-
URL = "
http://bioinformatics.oxfordjournals.org/content/early/2004/07/15/bioinformatics.bth414.full.pdf",
-
DOI = "
doi:10.1093/bioinformatics/bth414",
-
abstract = "Motivation: In order to design effective HIV
inhibitors, studying and understanding the mechanism of
HIV protease cleavage specification is critical.
Various methods have been developed to explore the
specificity of HIV protease cleavage activity. However,
success in both extracting discriminant rules and
maintaining high prediction accuracy is still
challenging. The earlier study had employed genetic
programming with a min-max scoring function to extract
discriminant rules with success. However, the decision
will finally be degenerated to one residue making
further improvement of the prediction accuracy
difficult. The challenge of revising the min-max
scoring function so as to improve the prediction
accuracy motivated this study.
Results: This paper has designed a new scoring function called a sum-product function for extracting HIV protease cleavage discriminant rules using genetic programming methods. The experiments show that the new scoring function is superior to the min-max scoring function. Availability: The software package can be obtained by request to Dr Zheng Rong Yang.
Contact: z.r.yang@ex.ac.uk",
- notes = "http://bioinformatics.oxfordjournals.org/cgi/content/abstract/20/18/3398 1 Department of Computer Science and 2 Department of Biology Sciences, Exeter University, UK PMID: 15256407 [PubMed - indexed for MEDLINE]",
