Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Symbolic Discriminant Analysis for Mining Gene Expression Patterns
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Cancer classification using gene expression data
Information Systems - Special issue: Data management in bioinformatics
Genetic Programming for Mining DNA Chip Data from Cancer Patients
Genetic Programming and Evolvable Machines
Genetic programming for human oral bioavailability of drugs
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Genetic programming for computational pharmacokinetics in drug discovery and development
Genetic Programming and Evolvable Machines
Artificial Intelligence in Medicine
EvoBIO'07 Proceedings of the 5th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
A genetic embedded approach for gene selection and classification of microarray data
EvoBIO'07 Proceedings of the 5th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
On the use of genetic programming for the prediction of survival in cancer
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A Study on Gene Regulatory Network Reconstruction and Simulation
Proceedings of the 2011 conference on Neural Nets WIRN10: Proceedings of the 20th Italian Workshop on Neural Nets
A new evolutionary gene regulatory network reverse engineering tool
EvoBIO'11 Proceedings of the 9th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
A comparison of machine learning methods for the prediction of breast cancer
EvoBIO'11 Proceedings of the 9th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
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The ability to accurately classify cancer patients into risk classes, i.e. to predict the outcome of the pathology on an individual basis, is a key ingredient in making therapeutic decisions. In recent years gene expression data have been successfully used to complement the clinical and histological criteria traditionally used in such prediction. Many “gene expression signatures” have been developed, i.e. sets of genes whose expression values in a tumor can be used to predict the outcome of the pathology. Here we investigate the use of several machine learning techniques to classify breast cancer patients using one of such signatures, the well established 70-gene signature. We show that Genetic Programming performs significantly better than Support Vector Machines, Multilayered Perceptron and Random Forest in classifying patients from the NKI breast cancer dataset, and slightly better than the scoring-based method originally proposed by the authors of the seventy-gene signature. Furthermore, Genetic Programming is able to perform an automatic feature selection. Since the performance of Genetic Programming is likely to be improvable compared to the out-of-the-box approach used here, and given the biological insight potentially provided by the Genetic Programming solutions, we conclude that Genetic Programming methods are worth further investigation as a tool for cancer patient classification based on gene expression data.