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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
2
|
| pubmed:dateCreated |
1992-10-29
|
| pubmed:abstractText |
We have developed a new method for protein secondary structure prediction that achieves accuracies as high as 71.0%, the highest value yet reported. The main component of our method is a nearest-neighbor algorithm that uses a more sophisticated treatment of the feature space than standard nearest-neighbor methods. It calculates distance tables that allow it to produce real-valued distances between amino acid residues, and attaches weights to the instances to further modify the the structure of feature space. The algorithm, which is closely related to the memory-based reasoning method of Zhang et al., is simple and easy to train, and has also been applied with excellent results to the problem of identifying DNA promoter sequences.
|
| pubmed:language |
eng
|
| pubmed:journal | |
| pubmed:citationSubset |
IM
|
| pubmed:status |
MEDLINE
|
| pubmed:month |
Sep
|
| pubmed:issn |
0022-2836
|
| pubmed:author | |
| pubmed:issnType |
Print
|
| pubmed:day |
20
|
| pubmed:volume |
227
|
| pubmed:owner |
NLM
|
| pubmed:authorsComplete |
Y
|
| pubmed:pagination |
371-4
|
| pubmed:dateRevised |
2006-11-15
|
| pubmed:meshHeading | |
| pubmed:year |
1992
|
| pubmed:articleTitle |
Predicting protein secondary structure with a nearest-neighbor algorithm.
|
| pubmed:affiliation |
Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218.
|
| pubmed:publicationType |
Journal Article,
Research Support, U.S. Gov't, Non-P.H.S.
|