Using Machine Learning to Design and Interpret Gene-Expression Microarrays

@article{Molla2004UsingML,
  title={Using Machine Learning to Design and Interpret Gene-Expression Microarrays},
  author={Michael Molla and Michael Waddell and David Page and Jude W. Shavlik},
  journal={AI Magazine},
  year={2004},
  volume={25},
  pages={23-44}
}
Gene-expression microarrays, commonly called “gene chips,” make it possible to simultaneously measure the rate at which a cell or tissue is expressing – translating into a protein – each of its thousands of genes. One can use these comprehensive snapshots of biological activity to infer regulatory pathways in cells, identify novel targets for drug design, and improve the diagnosis, prognosis, and treatment planning for those suffering from disease. However, the amount of data this new… CONTINUE READING
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