Paper Available: ID of binding sites on E.coli genetic sequences

[Mr. David Bisant]

          FTP-host: archive.cis.ohio-state.edu
          FTP-filename: /pub/neuroprose/bisant.ribosome.ps.Z

The file bisant.ribosome.ps.Z is a copy of a paper recently
accepted by Nucleic Acids Research.  It is now available for
copying from the Neuroprose repository.  Hardcopies can be 
photocopied from the journal itself shortly.
(18 pages, compressed file size 89K)


Title:  Identification of Ribosome Binding Sites in
        Escherichia coli Using Neural Network Models

Authors:

David Bisant                        Jacob Maizel
Neuroscience Program (151 B)        National Cancer Institute, FCRF
Stanford University                 Bldg 469 Rm 151, PO Box B
Stanford, CA 94305                  Frederick, MD 21701
bisant at decatur.stanford.edu         jmaizel at ncifcrf.gov


Abstract:

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 Escherichia 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 to 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.

Keywords:  neural networks, ribosome binding sites, nucleic acid sequence
analysis, ROC, Escherichia coli


URL:
ftp://archive.cis.ohio-state.edu/pub/neuroprose/bisant.ribosome.ps.Z