Evolving connectionist systems for knowledge discovery from gene expression data of cancer tissue

@article{Futschik2003EvolvingCS,
  title={Evolving connectionist systems for knowledge discovery from gene expression data of cancer tissue},
  author={Matthias E. Futschik and Anthony Reeve and Nikola K. Kasabov},
  journal={Artificial intelligence in medicine},
  year={2003},
  volume={28 2},
  pages={165-89}
}
Microarray techniques have made it possible to observe the expression of thousands of genes simultaneously. They have recently been applied to study gene expression patterns in tissue samples. This may lead to highly desirable improvements in the diagnosis and treatment of human diseases. Statistical and machine learning methods have recently been used to classify cancer tissue based on gene expression data. Although some of these methods have achieved a high degree of accuracy, they generally… CONTINUE READING