Linked Life Data

16568539

Source:http://linkedlifedata.com/resource/pubmed/id/16568539

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2006-3-28
pubmed:abstractText
This paper presents a composite multi-layer classifier system for predicting the subcellular localization of proteins based on their amino acid sequence. The work is an extension of our previous predictor PProwler v1.1 which is itself built upon the series of predictors SignalP and TargetP. In this study we outline experiments conducted to improve the classifier design. The major improvement came from using Support Vector machines as a "smart gate" sorting the outputs of several different targeting peptide detection networks. Our final model (PProwler v1.2) gives MCC values of 0.873 for non-plant and 0.849 for plant proteins. The model improves upon the accuracy of our previous subcellular localization predictor (PProwler v1.1) by 2% for plant data (which represents 7.5% improvement upon TargetP).
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
0219-7200
pubmed:author
pubmed:issnType
Print
pubmed:volume
4
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1-18
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Detecting and sorting targeting peptides with neural networks and support vector machines.
pubmed:affiliation
School of Information Technology and Electrical Engineering, The University of Queensland, QLD 4072, Australia. jhawkins@itee.uq.edu.au
pubmed:publicationType
Journal Article