Neural network based pattern recognition for sequenced DNA autoradiograms

Summary form only given. The three-layer, backward error propagation neural network has been applied to the problem of reading sequenced DNA autoradiograms. The network is used for band identification by extracting two features: band intensity level and band intensity gradient. A training set of 16000 12×12 pixel patterns is generated using an autoradiogram degradation model that accounts for radioisotope source crossfire, background, diffusion, contrast, surface stress artifacts, film grain, and quantum and convolutional noise. Trained with those patterns, the network successfully classified images from five previously unseen autoradiograms according to whether these two low-level features were present and provided the degree to which the features were present or absent