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Application of artificial neural networks for prokaryotic transcription terminator prediction
Author(s) -
T. Murlidharan Nair,
Sanjeev S. Tambe,
Bhaskar D. Kulkarni
Publication year - 1994
Publication title -
febs letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.593
H-Index - 257
eISSN - 1873-3468
pISSN - 0014-5793
DOI - 10.1016/0014-5793(94)00489-7
Subject(s) - terminator (solar) , artificial neural network , computer science , binary number , coding (social sciences) , artificial intelligence , computational biology , machine learning , biology , mathematics , statistics , physics , arithmetic , ionosphere , astronomy
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%.