Linked Life Data

19169652

Source:http://linkedlifedata.com/resource/pubmed/id/19169652

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2009-8-4
pubmed:abstractText
Apoptosis proteins play an essential role in regulating a balance between cell proliferation and death. The successful prediction of subcellular localization of apoptosis proteins directly from primary sequence is much benefited to understand programmed cell death and drug discovery. In this paper, by use of Chou's pseudo amino acid composition (PseAAC), a total of 317 apoptosis proteins are predicted by support vector machine (SVM). The jackknife cross-validation is applied to test predictive capability of proposed method. The predictive results show that overall prediction accuracy is 91.1% which is higher than previous methods. Furthermore, another dataset containing 98 apoptosis proteins is examined by proposed method. The overall predicted successful rate is 92.9%.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Sep
pubmed:issn
1572-8358
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
57
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
321-30
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Prediction of subcellular localization of apoptosis protein using Chou's pseudo amino acid composition.
pubmed:affiliation
Center for Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China. hlin@uestc.edu.cn
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't