PHD: predicting one-dimensional protein structure by profile-based neural networks (Q29614390)

https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=pubmed&retmode=json&id=8743704

artificial neural network

30 April 2017

main subject

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Rost B

1

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https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=pubmed&retmode=json&id=8743704

https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=pubmed&retmode=json&id=8743704

30 April 2017

language of work or name

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30 April 2017

publication date

1996

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https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=pubmed&retmode=json&id=8743704

30 April 2017

published in

Methods in Enzymology

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https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=pubmed&retmode=json&id=8743704

30 April 2017

volume

266

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https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=pubmed&retmode=json&id=8743704

30 April 2017

page(s)

525-39

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https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=pubmed&retmode=json&id=8743704

The SWISS-PROT protein sequence data bank: current status

30 April 2017

cites work

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Progress in protein structure prediction?

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Prediction of protein secondary structure at better than 70% accuracy

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Combining evolutionary information and neural networks to predict protein secondary structure

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Transmembrane helices predicted at 95% accuracy

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Progress of 1D protein structure prediction at last

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Redefining the goals of protein secondary structure prediction

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Structure of the Pl3K SH3 domain and analysis of the SH3 family

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Molecular modelling of the Norrie disease protein predicts a cystine knot growth factor tertiary structure

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