Linked Life Data

17470363

Source:http://linkedlifedata.com/resource/pubmed/id/17470363

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2007-5-15
pubmed:abstractText
Protein SUMO modification is an important post-translational modification and the optimization of prediction methods remains a challenge. Here, by using Support Vector Machines algorithm (SVM), a novel computational method was developed for SUMO modification site prediction based on Sequential Forward Selection (SFS) of hundreds of amino acid properties, which are collected by Amino Acid Index database (http://www.genome.jp/aaindex). Our method also compares with the 0/1 system, in which the 20 amino acids are represented by 20-dimensional vectors (A = 00000000000000000001, C = 00000000000000000010 and so on). The overall accuracy of leave-one-out cross-validation for our method reaches 89.18%, which is higher than 0/1 system. It indicated that the SUMO modification prediction process is highly related to the amino acid property and this approach here provide a helpful tool for further investigation of the SUMO modification and identification of sumoylation sites in proteins. The software is available at http://www.biosino.org/sumo.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
0006-291X
pubmed:author
pubmed:issnType
Print
pubmed:day
22
pubmed:volume
358
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
136-9
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Predicting the protein SUMO modification sites based on Properties Sequential Forward Selection (PSFS).
pubmed:affiliation
Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, China.
pubmed:publicationType
Journal Article