|Student Name:||Capt Edward Brouch|
|Thesis:||Artificial Neural Network Prediction of Chemical-Disease Relationships using Readily Available Chemical Properties|
|Location:||Rm 216, Bldg 646|
|Date & Time:||02/13/2014 at 1300|
|Abstract:|| The natural environment is burdened with a broad range of toxic chemicals, and there is a need to develop a tool that can accelerate the pace at which we learn how chemicals impact disease. This work developed an artificial neural network (ANN) based model that constructed chemical-disease relationships for chemicals found in the Comparative Toxicogenomics Database. A new chemical classification system was created to identify chemicals with a unique number that is directly related to the structural characteristics of the chemical. The ANN model was successfully trained and tested to associated 75 chemicals with 27 disease categories. Simulations with training-validation-testing ratios of 70-15-15 percent produced coefficients of determination equal to 0.99, and the Levenberg-Marquardt backpropagation function provided the best network performance. The ANN was also used to evaluate chemical-disease relationships for three uncurated chemicals. This work showed that ANNs have the potential to predict disease-associations for new chemicals and to guide research for existing chemicals that require toxicological testing.