Article

Exploring relationships between genotype and oral cancer development through XCS

Authors:

Alessandro Passaro,

Flavio Baronti,

Valentina MagginiAuthors Info & Claims

GECCO '05: Proceedings of the 7th annual workshop on Genetic and evolutionary computation

Pages 147 - 151

https://doi.org/10.1145/1102256.1102289

Published: 25 June 2005 Publication History

Abstract

In medical research, being able to justify decisions is generally as important as taking the right ones. Interpretability is then one of the chief characteristics a learning algorithm must have, in order to be successfully applied to a medical data set. Other important features are seamless treatment of different data types, and ability to cope well with missing values. XCS and decision trees both appear to have this desirable characteristics; we compared them on a data set regarding Head and neck squamous cell carcinoma (HNSCC). This kind of oral cancer already been found to be associated with smoking and alcohol drinking habits. However the individual risk could be modified by genetic polymorphisms of enzymes involved in the metabolism of tobacco carcinogens and in the DNA repair mechanisms. To study this relationship, the data set comprised demographic and life-style (age, gender, smoke and alcohol), and genetic data (the individual genotype of 11 polymorphic genes), with the information on 124 HNSCC patients and 231 healthy controls. Results with both algorithms are presented and analyzed.

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Cited By

Huang PChen LChung WSheu CHsiao TTsai J(2022)Toward Evaluating Critical Factors of Extubation Outcome with XCSR-Generated RulesBioengineering10.3390/bioengineering91107019:11(701)Online publication date: 17-Nov-2022
https://doi.org/10.3390/bioengineering9110701
Chen LLee PHsiao TEsparcia-Alcázar A(2015)A Novel Representation of Classifier Conditions Named Sensory Tag for the XCS in Multistep ProblemsProceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation10.1145/2739482.2768446(973-980)Online publication date: 11-Jul-2015
https://dl.acm.org/doi/10.1145/2739482.2768446
Chen LLee PHsiao T(2015)A sensor tagging approach for reusing building blocks of knowledge in learning classifier systems2015 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2015.7257256(2953-2960)Online publication date: May-2015
https://doi.org/10.1109/CEC.2015.7257256
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Index Terms

Exploring relationships between genotype and oral cancer development through XCS
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Machine learning

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Published In

cover image ACM Conferences

GECCO '05: Proceedings of the 7th annual workshop on Genetic and evolutionary computation

June 2005

431 pages

ISBN:9781450378000

DOI:10.1145/1102256

Conference Chair:
Franz Rothlauf
University of Mannheim, Germany

Copyright © 2005 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

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Publication History

Published: 25 June 2005

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GECCO05

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GECCO05: Genetic and Evolutionary Computation Conference

June 25 - 26, 2005

D.C., Washington

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Huang PChen LChung WSheu CHsiao TTsai J(2022)Toward Evaluating Critical Factors of Extubation Outcome with XCSR-Generated RulesBioengineering10.3390/bioengineering91107019:11(701)Online publication date: 17-Nov-2022
https://doi.org/10.3390/bioengineering9110701
Chen LLee PHsiao TEsparcia-Alcázar A(2015)A Novel Representation of Classifier Conditions Named Sensory Tag for the XCS in Multistep ProblemsProceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation10.1145/2739482.2768446(973-980)Online publication date: 11-Jul-2015
https://dl.acm.org/doi/10.1145/2739482.2768446
Chen LLee PHsiao T(2015)A sensor tagging approach for reusing building blocks of knowledge in learning classifier systems2015 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2015.7257256(2953-2960)Online publication date: May-2015
https://doi.org/10.1109/CEC.2015.7257256
Chang SAbdul-Kareem SMerican AZain R(2013)Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methodsBMC Bioinformatics10.1186/1471-2105-14-17014:1Online publication date: 31-May-2013
https://doi.org/10.1186/1471-2105-14-170
Lin HLee PHsiao TAlba E(2013)The subsumption mechanism for XCS using code fragmented conditionsProceedings of the 15th annual conference companion on Genetic and evolutionary computation10.1145/2464576.2482706(1275-1282)Online publication date: 6-Jul-2013
https://dl.acm.org/doi/10.1145/2464576.2482706
Lee PHsiao T(2011)Applying XCS on time variant problem: Separates thinking from doing2011 Third World Congress on Nature and Biologically Inspired Computing10.1109/NaBIC.2011.6089616(348-352)Online publication date: Oct-2011
https://doi.org/10.1109/NaBIC.2011.6089616
Chang Siow-Wee Sameem AMohd Zain RMerican A(2010)A Bootstrap-ANFIS framework for oral cancer prognosis based on clinical and genomic markers2010 3rd International Conference on Computer Science and Information Technology10.1109/ICCSIT.2010.5564094(487-491)Online publication date: Jul-2010
https://doi.org/10.1109/ICCSIT.2010.5564094
Smith S(2010)A Review of Medical Applications of Genetic and Evolutionary ComputationGenetic and Evolutionary Computation10.1002/9780470973134.ch3(17-43)Online publication date: 3-Dec-2010
https://doi.org/10.1002/9780470973134.ch3
Baronti FStarita A(2007)Hypothesis testing with classifier systems for rule-based risk predictionProceedings of the 5th European conference on Evolutionary computation, machine learning and data mining in bioinformatics10.5555/1761486.1761489(24-34)Online publication date: 11-Apr-2007
https://dl.acm.org/doi/10.5555/1761486.1761489
Baronti FStarita A(2007)Hypothesis Testing with Classifier Systems for Rule-Based Risk PredictionEvolutionary Computation,Machine Learning and Data Mining in Bioinformatics10.1007/978-3-540-71783-6_3(24-34)Online publication date: 2007
https://doi.org/10.1007/978-3-540-71783-6_3
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