A computer program is trained on examples of good and bad promoters. Weights are flexible and optimized to discriminate between the training set examples. Such methods do not seem to give appreciably better results than a maximum likelihood approach. However, they can be more easily adapted to include additional information about what makes a good motif. Consider the case of promoter recognition.
Many promoters require additional proteins to initiate transcription.
These recognize their own DNA motifs, usually located near the sigma factor element. DNA curvature is also often a factor. DNA curvature can be calculated by accumulating AA and TT pairs. This approach has been incorporated into the analysis of promoter score. Information Content of Sequence Motifs Information theory can be used to analyze sequence motifs.