Source:http://linkedlifedata.com/resource/pubmed/id/11835497
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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:dateCreated |
2002-2-8
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| pubmed:abstractText |
EVA is a web-based server that evaluates automatic structure prediction servers continuously and objectively. Since June 2000, EVA collected more than 20,000 secondary structure predictions. The EVA sets sufficed to conclude that the field of secondary structure prediction has advanced again. Accuracy increased substantially in the 1990s through using evolutionary information taken from the divergence of proteins in the same structural family. Recently, the evolutionary information resulting from improved searches and larger databases has again boosted prediction accuracy by more than 4% to its current height around 76% of all residues predicted correctly in one of the three states: helix, strand, or other. The best current methods solved most of the problems raised at earlier CASP meetings: All good methods now get segments right and perform well on strands. Is the recent increase in accuracy significant enough to make predictions even more useful? We believe the answer is affirmative. What is the limit of prediction accuracy? We shall see. All data are available through the EVA web site at [cubic.bioc.columbia.edu/eva/]. The raw data for the results presented are available at [eva]/sec/bup_common/2001_02_22/.
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| pubmed:language |
eng
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| pubmed:journal | |
| pubmed:citationSubset |
IM
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| pubmed:status |
MEDLINE
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| pubmed:issn |
0887-3585
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| pubmed:author | |
| pubmed:copyrightInfo |
Copyright 2002 Wiley Liss, Inc.
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| pubmed:issnType |
Print
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| pubmed:volume |
Suppl 5
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| pubmed:owner |
NLM
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| pubmed:authorsComplete |
Y
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| pubmed:pagination |
192-9
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| pubmed:meshHeading | |
| pubmed:year |
2001
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| pubmed:articleTitle |
EVA: large-scale analysis of secondary structure prediction.
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| pubmed:affiliation |
CUBIC, Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York 10032, USA. rost@columbia.edu
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| pubmed:publicationType |
Journal Article
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