Genetic programming for human oral bioavailability of drugs
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{1144042,
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author = "Francesco Archetti and Stefano Lanzeni and
Enza Messina and Leonardo Vanneschi",
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title = "Genetic programming for human oral bioavailability of
drugs",
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booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference
on Genetic and evolutionary computation",
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year = "2006",
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editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and
Vladan Babovic and Christian Blum and Peter Bosman and
Martin V. Butz and Carlos {Coello Coello} and
Dipankar Dasgupta and Sevan G. Ficici and James Foster and
Arturo Hernandez-Aguirre and Greg Hornby and
Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and
Franz Rothlauf and Conor Ryan and Dirk Thierens",
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volume = "1",
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ISBN = "1-59593-186-4",
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pages = "255--262",
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address = "Seattle, Washington, USA",
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URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p255.pdf",
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DOI = "doi:10.1145/1143997.1144042",
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publisher = "ACM Press",
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publisher_address = "New York, NY, 10286-1405, USA",
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month = "8-12 " # jul,
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organisation = "ACM SIGEVO (formerly ISGEC)",
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keywords = "genetic algorithms, genetic programming, Biological
Applications, bioavailability, bioinformatics,
complexity measures, molecular descriptors, performance
measures, SVM, ANN, LLSR, CFS, PCA, AIC, feature
selection, SMILES",
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size = "8 pages",
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abstract = "Automatically assessing the value of bioavailability
from the chemical structure of a molecule is a very
important issue in biomedicine and pharmacology. In
this paper, we present an empirical study of some well
known Machine Learning techniques, including various
versions of Genetic Programming, which have been
trained to this aim using a dataset of molecules with
known bioavailability. Genetic Programming has proven
the most promising technique among the ones that have
been considered both from the point of view of the
accurateness of the solutions proposed, of the
generalisation capabilities and of the correlation
between predicted data and correct ones. Our work
represents a first answer to the demand for
quantitative bioavailability estimation methods
proposed in literature, since the previous
contributions focus on the classification of molecules
into classes with similar bioavailability. Categories
and Subject Descriptors",
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notes = "GECCO-2006 A joint meeting of the fifteenth
international conference on genetic algorithms
(ICGA-2006) and the eleventh annual genetic programming
conference (GP-2006).
ACM Order Number 910060
Winner best paper.",
- }
Genetic Programming entries for
Francesco Archetti
Stefano Lanzeni
Enza Messina
Leonardo Vanneschi
Citations