Identification of ribosome binding sites in Escherichia coli using neural network models.
This study investigated the use of neural networks in the identification of Escherichia coli ribosome binding sites. The recognition of these sites based on primary sequence data is difficult due to the multiple determinants that define them. Additionally, secondary structure plays a significant role in the determination of the site and this information is difficult to include in the models. Efforts to solve this problem have so far yielded poor results. A new compilation of E. coli ribosome binding sites was generated for this study. Feedforward backpropagation networks were applied to their identification. Perceptrons were also applied, since they have been the previous best method since 1982. Evaluation of performance for all the neural networks and perceptrons was determined by ROC analysis. The neural network provided significant improvement in the recognition of these sites when compared with the previous best method, finding less than half the number of false positives when both models were adjusted to find an equal number of actual sites. The best neural network used an input window of 101 nucleotides and a single hidden layer of 9 units. Both the neural network and the perceptron trained on the new compilation performed better than the original perceptron published by Stormo et al. in 1982.
Bisant
D
D
Neuroscience Program (151 B), Stanford University, CA 94305, USA.
Maizel
J
J
D001483:Base Sequence; D001665:Binding Sites; D004926:Escherichia coli / Q000737:chemistry / Q000378:metabolism*; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D012270:Ribosomes / Q000378:metabolism*