Linked Life Data

18974075

Source:http://linkedlifedata.com/resource/pubmed/id/18974075

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
23
pubmed:dateCreated
2008-11-25
pubmed:abstractText
Regulatory proteases modulate proteomic dynamics with a spectrum of specificities against substrate proteins. Predictions of the substrate sites in a proteome for the proteases would facilitate understanding the biological functions of the proteases. High-throughput experiments could generate suitable datasets for machine learning to grasp complex relationships between the substrate sequences and the enzymatic specificities. But the capability in predicting protease substrate sites by integrating the machine learning algorithms with the experimental methodology has yet to be demonstrated.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
1367-4811
pubmed:author
pubmed:issnType
Electronic
pubmed:day
1
pubmed:volume
24
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
2691-7
pubmed:dateRevised
2009-11-4
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Protease substrate site predictors derived from machine learning on multilevel substrate phage display data.
pubmed:affiliation
Institute of Information Science, Academia Sinica, Taipei 115, Taiwan.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't