Linked Life Data

10356977

Source:http://linkedlifedata.com/resource/pubmed/id/10356977

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
1999-6-30
pubmed:abstractText
A novel method was introduced to predict protein subcellular locations from sequences. Using sequence data, this method achieved a prediction accuracy higher than previous methods based on the amino acid composition. For three subcellular locations in a prokaryotic organism, the overall prediction accuracy reached 89.1%. For eukaryotic proteins, prediction accuracies of 73.0% and 78.7% were attained within four and three location categories, respectively. These results demonstrate the applicability of this relative simple method and possible improvement of prediction for the protein subcellular location.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
0014-5793
pubmed:author
pubmed:issnType
Print
pubmed:day
14
pubmed:volume
451
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
23-6
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
1999
pubmed:articleTitle
Prediction of protein subcellular locations using Markov chain models.
pubmed:affiliation
National Laboratory of Biomacromolecules, Institute of Biophysics, Academia Sinica, Beijing, China. zxwang@sun5.ibp.ac.cn
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't