Linked Life Data

17436325

Source:http://linkedlifedata.com/resource/pubmed/id/17436325

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2007-5-28
pubmed:abstractText
A number of methods for predicting levels of solvent accessibility or accessible surface area (ASA) of amino acid residues in proteins have been developed. These methods either predict regularly spaced states of relative solvent accessibility or an analogue real value indicating relative solvent accessibility. While discrete states of exposure can be easily obtained by post prediction assignment of thresholds to the predicted or computed real values of ASA, the reverse, that is, obtaining a real value from quantized states of predicted ASA, is not straightforward as a two-state prediction in such cases would give a large real valued errors. However, prediction of ASA into larger number of ASA states and then finding a corresponding scheme for real value prediction may be helpful in integrating the two approaches of ASA prediction. We report a novel method of obtaining numerical real values of solvent accessibility, using accumulation cutoff set and support vector machine. This so-called SVM-Cabins method first predicts discrete states of ASA of amino acid residues from their evolutionary profile and then maps the predicted states onto a real valued linear space by simple algebraic methods. Resulting performance of such a rigorous approach using 13-state ASA prediction is at least comparable with the best methods of ASA prediction reported so far. The mean absolute error in this method reaches the best performance of 15.1% on the tested data set of 502 proteins with a coefficient of correlation equal to 0.66. Since, the method starts with the prediction of discrete states of ASA and leads to real value predictions, performance of prediction in binary states and real values are simultaneously optimized.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
1097-0134
pubmed:author
pubmed:copyrightInfo
2007 Wiley-Liss, Inc.
pubmed:issnType
Electronic
pubmed:day
1
pubmed:volume
68
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
82-91
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
SVM-Cabins: prediction of solvent accessibility using accumulation cutoff set and support vector machine.
pubmed:affiliation
Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't