On the Use of Estimated Tumor Marker Classifications in Tumor Diagnosis Prediction - A Case Study for Breast Cancer
Created by W.Langdon from
gp-bibliography.bib Revision:1.8414
- @Article{2399,
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author = "Stephan M. Winkler and Michael Affenzeller and
Gabriel Kronberger and Michael Kommenda and Stefan Wagner and
Viktoria Dorfer and Witold Jacak and Herbert Stekel",
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title = "On the Use of Estimated Tumor Marker Classifications
in Tumor Diagnosis Prediction - A Case Study for Breast
Cancer",
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journal = "International Journal of Simulation and Process
Modelling",
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year = "2011",
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pages = "29--41",
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month = sep,
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keywords = "genetic algorithms, genetic programming, EAs,
evolutionary algorithms, medical data analysis, tumour
marker modelling, data mining",
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ISSN = "1740-2123",
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address = "Roma, Italy",
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booktitle = "Proceedings of 23rd IEEE European Modeling \&
Simulation Symposium EMSS 2011",
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URL = "
https://www.inderscienceonline.com/doi/abs/10.1504/IJSPM.2013.055192",
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DOI = "
doi:10.1504/IJSPM.2013.055192",
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abstract = "we describe the use of tumour marker estimation models
in the prediction of tumour diagnoses. In previous
works, we have identified classification models for
tumour markers that can be used for estimating tumour
marker values on the basis of standard blood
parameters. These virtual tumour markers are now used
in combination with standard blood parameters for
learning classifiers that are used for predicting
tumour diagnoses. Several data-based modelling
approaches implemented in HeuristicLab have been
applied for identifying estimators for selected tumour
markers and cancer diagnoses: Linear regression,
k-nearest neighbour (k-NN) learning, artificial neural
networks (ANNs) and support vector machines (SVMs) (all
optimised using evolutionary algorithms), as well as
genetic programming (GP). We have applied these
modelling approaches for identifying models for breast
cancer diagnoses; in the results section, we summarise
classification accuracies for breast cancer and we
compare classification results achieved by models that
use measured marker values as well as models that use
virtual tumour markers.",
- }
Genetic Programming entries for
Stephan M Winkler
Michael Affenzeller
Gabriel Kronberger
Michael Kommenda
Stefan Wagner
Viktoria Dorfer
Witold Jacak
Herbert Stekel
Citations