Linked Life Data

9322023

Source:http://linkedlifedata.com/resource/pubmed/id/9322023

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
1997-12-11
pubmed:abstractText
This work demonstrates new techniques developed for the prediction of protein folding class in the context of the most comprehensive Structural Classification of Proteins (SCOP). The prediction method uses global descriptors of a protein in terms of the physical, chemical and structural properties of its constituent amino acids. Neural networks are utilized to combine these descriptors in a specific way to discriminate members of a given folding class from members of all other classes. It is shown that a specific amino acid's properties work completely differently on different folding classes. This creates the possibility of finding an individual set of descriptors that works best on a particular folding class.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1553-0833
pubmed:author
pubmed:issnType
Print
pubmed:volume
5
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
104-7
pubmed:dateRevised
2007-11-15
pubmed:meshHeading
pubmed:year
1997
pubmed:articleTitle
Protein folding class predictor for SCOP: approach based on global descriptors.
pubmed:affiliation
Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. ildubchak, shkim@lbl.gov
pubmed:publicationType
Journal Article, Comparative Study