Learning to Predict Reading Frames in E . coli DNA


Two fundamental problems in analyzing DNA sequences are (1) locating the regions of a DNA sequence that encode proteins, and (2) determining the reading frame for each region. We investigate using artiicial neural networks (ANNs) to nd coding regions, determine reading frames, and detect frameshift errors in E. coli DNA sequences. We describe our adaptation of the approach used by Uberbacher and Mural to identify coding regions in human DNA, and we compare the performance of ANNs to several conventional methods for predicting reading frames. Our experiments demonstrate that ANNs can outperform these conventional approaches.

Cite this paper

@inproceedings{Craven1993LearningTP, title={Learning to Predict Reading Frames in E . coli DNA}, author={Mark Craven and Jude W. Shavlik}, year={1993} }