Applications of genetic programming in cancer research

The theory of Darwinian evolution is the fundamental keystones of modern biology. Late in the last century, computer scientists began adapting its principles, in particular natural selection, to complex computational challenges, leading to the emergence of evolutionary algorithms. The conceptual mod...

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Published inThe international journal of biochemistry & cell biology Vol. 41; no. 2; pp. 405 - 413
Main Authors Worzel, William P., Yu, Jianjun, Almal, Arpit A., Chinnaiyan, Arul M.
Format Journal Article
LanguageEnglish
Published Netherlands Elsevier Ltd 01.02.2009
Subjects
Algorithms
Biological Evolution
Cancer classification
Cancer diagnosis
Cancer prognosis
Computational Biology - methods
Computer Simulation
Evolution, Molecular
Evolutionary algorithms
Genetic programming
Models, Genetic
Neoplasms - genetics
Evolutionary algorithms
Cancer diagnosis
Cancer classification
Genetic programming
Cancer prognosis
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ISSN1357-2725
1878-5875
1878-5875
DOI10.1016/j.biocel.2008.09.025

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Summary:The theory of Darwinian evolution is the fundamental keystones of modern biology. Late in the last century, computer scientists began adapting its principles, in particular natural selection, to complex computational challenges, leading to the emergence of evolutionary algorithms. The conceptual model of selective pressure and recombination in evolutionary algorithms allow scientists to efficiently search high dimensional space for solutions to complex problems. In the last decade, genetic programming has been developed and extensively applied for analysis of molecular data to classify cancer subtypes and characterize the mechanisms of cancer pathogenesis and development. This article reviews current successes using genetic programming and discusses its potential impact in cancer research and treatment in the near future.
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ISSN:1357-2725
1878-5875
1878-5875
DOI:10.1016/j.biocel.2008.09.025