(Q24633439)

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Training back-propagation neural networks to define and detect DNA-binding sites

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title

Training back-propagation neural networks to define and detect DNA-binding sites (English)

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main subject

artificial neural network

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M C O'Neill

1

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language of work or name

English

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publication date

25 January 1991

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published in

Nucleic Acids Research

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volume

19

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page(s)

313-8

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issue

2

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cites work

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Information content of binding sites on nucleotide sequences

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Use of the ‘Perceptron’ algorithm to distinguish translational initiation sites inE. coli

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Organization of transcriptional signals in plasmids pBR322 and pACYC184.

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Sequence determinants of promoter activity

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Escherichia colipromoter sequences predictin vitroRNA polymerase selectivity

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Computer methods to locate signals in nucleic acid sequences

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Compilation and analysis ofEscherichia colipromoter DNA sequences

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Promoters recognized by Escherichia coli RNA polymerase selected by function: highly efficient promoters from bacteriophage T5.

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E. coli promoter spacer regions contain nonrandom sequences which correlate to spacer length

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Selection of DNA binding sites by regulatory proteins. Statistical-mechanical theory and application to operators and promoters.

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Escherichia coli promoters. II. A spacing class-dependent promoter search protocol

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Escherichia coli promoters. I. Consensus as it relates to spacing class, specificity, repeat substructure, and three-dimensional organization

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inferred from PubMed ID database lookup

Consensus methods for finding and ranking DNA binding sites. Application to Escherichia coli promoters

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based on heuristic

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An electron microscopic comparison of transcription on linear and superhelical DNA

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12 December 2020

based on heuristic

inferred from PubMed ID database lookup

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21142182

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(Q24633439)

Training back-propagation neural networks to define and detect DNA-binding sites

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