Robust regression and outlier detection
Robust regression and outlier detection
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
Foundations of genetic programming
Foundations of genetic programming
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
General schema theory for genetic programming with subtree-swapping crossover: part I
Evolutionary Computation
General schema theory for genetic programming with subtree-swapping crossover: Part II
Evolutionary Computation
Genetic Programming and Evolvable Machines
Frequent Subtree Mining - An Overview
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
Extracting gene regulation information for cancer classification
Pattern Recognition
Genetic programming for computational pharmacokinetics in drug discovery and development
Genetic Programming and Evolvable Machines
Improving symbolic regression with interval arithmetic and linear scaling
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Evolutionary biclustering with correlation for gene interaction networks
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Repeated patterns in tree genetic programming
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
Bioinformatics with soft computing
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Evolutionary symbolic discovery for bioinformatics, systems and synthetic biology
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
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Statistical methods, and in particular machine learning, have been increasingly used in the drug development workflow. Among the existing machine learning methods, we have been specifically concerned with genetic programming. We present a genetic programming-based framework for predicting anticancer therapeutic response. We use the NCI-60 microarray dataset and we look for a relationship between gene expressions and responses to oncology drugs Fluorouracil, Fludarabine, Floxuridine and Cytarabine. We aim at identifying, from genomic measurements of biopsies, the likelihood to develop drug resistance. Experimental results, and their comparison with the ones obtained by Linear Regression and Least Square Regression, hint that genetic programming is a promising technique for this kind of application. Moreover, genetic programming output may potentially highlight some relations between genes which could support the identification of biological meaningful pathways. The structures that appear more frequently in the ''best'' solutions found by genetic programming are presented.