A genetic programming method for feature mapping to improve prediction of HIV-1 protease cleavage site
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- @Article{FATHI:2018:ASC,
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author = "Abdolhossein Fathi and Rasool Sadeghi",
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title = "A genetic programming method for feature mapping to
improve prediction of {HIV-1} protease cleavage site",
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journal = "Applied Soft Computing",
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year = "2018",
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volume = "72",
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pages = "56--64",
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month = nov,
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keywords = "genetic algorithms, genetic programming, SVM, Amino
acid encoding, Feature mapping, Amino acid sequence
cleavage prediction",
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ISSN = "1568-4946",
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URL = "http://www.sciencedirect.com/science/article/pii/S156849461830379X",
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DOI = "doi:10.1016/j.asoc.2018.06.045",
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code_url = "https://github.com/rasool-sadeghi/Encoding-and-Prediction-of-Cleavage-of-Amino-Acid-Sequences-by-HIV-1",
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abstract = "The human immunodeficiency virus (HIV) is the cause of
acquired immunodeficiency syndrome (AIDS), which has
profound implications in terms of both economic burden
and loss of life. Modeling and examination of the HIV
protease cleavage of amino acid sequences can
contribute to control of this disease and production of
more effective drugs. The present paper introduces a
new method for encoding and characterization of amino
acid sequences and a new model for the prediction of
amino acid sequence cleavage by HIV protease. The
proposed encoding scheme uses a combination of amino
acids' spatial and structural features in conjunction
with 20 amino acid sequences to make sure that their
physicochemical and sequencing features are all taken
into account. The proposed HIV-1 amino acid cleavage
prediction model is developed with the combination of
genetic programming and support vector machine. The
results of evaluations performed on various datasets
demonstrate the superior performance of the proposed
encoding and better accuracy of the proposed HIV-1
cleavage prediction model as compared to the
state-of-the-art methods",
- }
Genetic Programming entries for
Abdolhossein Fathi
Rasool Sadeghi
Citations