Linked Life Data

17500036

Source:http://linkedlifedata.com/resource/pubmed/id/17500036

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2007-5-25
pubmed:abstractText
Classification for enzymes is a prerequisite for understanding their function. Here, an automatic enzyme identifier based on support vector machine (SVM) with feature vectors from protein functional domain composition was built to identify enzymes and further a classifier to classify enzymes into six different classes: oxidoreductase, transferase, hydrolase, lyase, isomerase and ligase. Jackknife cross-validation test was adopted to evaluate the performance of our classifier. The 86.03% success rate achieved for enzyme/non-enzyme identification and 91.32% for enzyme classification, which is much better than that of the BLAST and PSI-BLAST based method, also outperforms several existed works. The results indicate that protein functional domain composition is able to capture the major features which facilitate the identification/classification of proteins, thus demonstrating that our predictor could be a more effective and promising high-throughput method in enzyme research. Moreover, a web-based software Enzyme Classification System (ECS) for identification as well as classification of enzymes can be accessed at: http://pcal.biosino.org/.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
1476-9271
pubmed:author
pubmed:issnType
Print
pubmed:volume
31
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
226-32
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
ECS: an automatic enzyme classifier based on functional domain composition.
pubmed:affiliation
Bioinformatics Center, Key Lab of Molecular Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, China.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't