Can circulating matrix metalloproteinases be predictors of breast cancer? a neural network modeling study

  • Authors:

  • H. Hu;S. B. Somiari;J. Copper;R. D. Everly;C. Heckman;R. Jordan;R. Somiari;J. Hooke;C. D. Shriver;M. N. Liebman

  • Affiliations:

  • Windber Research Institute, Windber, PA;Windber Research Institute, Windber, PA;NeuralWare, Pittsburgh, PA;NeuralWare, Pittsburgh, PA;Windber Research Institute, Windber, PA;Windber Research Institute, Windber, PA;Windber Research Institute, Windber, PA;Walter Reed Army Medical Center, Washington, DC;Walter Reed Army Medical Center, Washington, DC;Windber Research Institute, Windber, PA

  • Venue:
  • ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
  • Year:
  • 2005

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Abstract

At Windber Research Institute we have started research programs that use artificial neural networks (ANNs) in the study of breast cancer in order to identify heterogeneous data predictors of patient disease stages. As an initial effort, we have chosen matrix metalloproteinases (MMPs) as potential biomarker predictors. MMPs have been implicated in the early and late stage development of breast cancer. However, it is unclear whether these proteins hold predictive power for breast disease diagnosis, and we are not aware of any exploratory modeling efforts that address the question. Here we report the development of ANN models employing plasma levels of these proteins for breast disease predictions.