Genetic Programming and Other Machine Learning Approaches to Predict Median Oral Lethal Dose (LD50) and Plasma Protein Binding Levels (%PPB) of Drugs
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Archetti:2007:evobio,
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author = "Francesco Archetti and Stefano Lanzeni and
Enza Messina and Leonardo Vanneschi",
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title = "Genetic Programming and Other Machine Learning
Approaches to Predict Median Oral Lethal Dose (LD50)
and Plasma Protein Binding Levels (%PPB) of Drugs",
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booktitle = "EvoBIO 2007, Proceedings of the 5th European
Conference on Evolutionary Computation, Machine
Learning and Data Mining in Bioinformatics",
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year = "2007",
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editor = "Elena Marchiori and Jason H. Moore and
Jagath C. Rajapakse",
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volume = "4447",
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series = "Lecture Notes in Computer Science",
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pages = "11--23",
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address = "Valencia, Spain",
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publisher_address = "Berlin Heidelberg NewYork",
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month = apr # " 11-13",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "0302-9743",
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ISBN = "3-540-71782-X",
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ISBN-13 = "978-3-540-71782-9",
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DOI = "doi:10.1007/978-3-540-71783-6_2",
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abstract = "Computational methods allowing reliable
pharmacokinetics predictions for newly synthesised
compounds are critically relevant for drug discovery
and development. Here we present an empirical study
focusing on various versions of Genetic Programming and
other well known Machine Learning techniques to predict
Median Oral Lethal Dose (LD50) and Plasma Protein
Binding (%PPB) levels. Since these two parameters
respectively characterise the harmful effects and the
distribution into human body of a drug, their accurate
prediction is essential for the selection of effective
molecules. The obtained results confirm that Genetic
Programming is a promising technique for predicting
pharmacokinetics parameters, both from the point of
view of the accurateness and of the generalisation
ability.",
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notes = "EvoBIO2007",
- }
Genetic Programming entries for
Francesco Archetti
Stefano Lanzeni
Enza Messina
Leonardo Vanneschi
Citations