Artificial Neural Network Analysis in Preclinical Breast Cancer

OBJECTIVE: In this study, artificial neural network (ANN) analysis of virotherapy in preclinical breast cancer was investigated. MATERIALS AND METHODS: In this research article, a multilayer feed-forward neural network trained with an error back-propagation algorithm was incorporated in order to develop a predictive model. The input parameters of the model were virus dose, week and tamoxifen citrate, while tumor weight was included in the output parameter. Two different training algorithms, namely quick propagation (QP) and Levenberg-Marquardt (LM), were used to train ANN. RESULTS: The results showed that the LM algorithm, with 3-9-1 arrangement is more efficient compared to QP. Using LM algorithm, the coefficient of determination (R(2)) between the actual and predicted values was determined as 0.897118 for all data. CONCLUSION: It can be concluded that this ANN model may provide good ability to predict the biometry information of tumor in preclinical breast cancer virotherapy. The results showed that the LM algorithm employed by Neural Power software gave the better performance compared with the QP and virus dose, and it is more important factor compared to tamoxifen and time (week).