Application of artificial neural networks for prokaryotic transcription terminator prediction

Artificial neural networks (ANN) to predict terminator sequences, based on a feed‐forward architecture and trained using the error back propagation technique, have been developed. The network uses two different methods for coding nucleotide sequences. In one the nucleotide bases are coded in binary while the other uses the electron—ion interaction potential values (EIIP) of the nucleotide bases. The latter strategy is new, property based and substantially reduces the network size. The prediction capacity of the artificial neural network using both coding strategies is more than 95%.

[1]  M J Sternberg,et al.  Prediction of structural and functional features of protein and nucleic acid sequences by artificial neural networks. , 1992, Biochemistry.

[2]  David A. Frank,et al.  A Theoretical Approach to the Preselection of Carcinogens and Chemical Carcinogenesis , 1981, The Yale Journal of Biology and Medicine.

[3]  E N Trifonov,et al.  A computer algorithm for testing potential prokaryotic terminators. , 1984, Nucleic acids research.

[4]  C. Savory.  Gastrointestinal motility in relation to spontaneous meal occurrence in domestic fowls , 1987, Appetite.

[5]  V. Veljković,et al.  Simple General-Model Pseudopotential , 1972 .

[6]  J. Zupan,et al.  Neural networks: A new method for solving chemical problems or just a passing phase? , 1991 .

[7]  J. Zupan,et al.  Neural Networks in Chemistry , 1993 .

[8]  R C Mann,et al.  An artificial intelligence approach to DNA sequence feature recognition. , 1992, Trends in biotechnology.

[9]  I. Cosic,et al.  Is it Possible to Analyze DNA and Protein Sequences by the Methods of Digital Signal Processing? , 1985, IEEE Transactions on Biomedical Engineering.

[10]  D Court,et al.  Regulatory sequences involved in the promotion and termination of RNA transcription. , 1979, Annual review of genetics.

[11]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[12]  P. V. von Hippel,et al.  Protein-nucleic acid interactions in transcription: a molecular analysis. , 1984, Annual review of biochemistry.

[13]  D. Greenwood,et al.  Solid State Theory , 1973, Nature.

[14]  G. Zhou,et al.  Neural network optimization for E. coli promoter prediction. , 1991, Nucleic acids research.

[15]  E N Trifonov,et al.  Terminators of transcription with RNA polymerase from Escherichia coli: what they look like and how to find them. , 1986, Journal of biomolecular structure & dynamics.

[16]  Benny Lautrup,et al.  A novel approach to prediction of the 3‐dimensional structures of protein backbones by neural networks , 1990, NIPS.

[17]  I. Cosic,et al.  Prediction of 'hot spots' in SV40 enhancer and relation with experimental data. , 1987, European journal of biochemistry.

[18]  E. Uberbacher,et al.  Locating protein-coding regions in human DNA sequences by a multiple sensor-neural network approach. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[19]  L Roberts,et al.  GRAIL seeks out genes buried in DNA sequence. , 1991, Science.