Linked Life Data

20625199

Source:http://linkedlifedata.com/resource/pubmed/id/20625199

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2010-7-13
pubmed:abstractText
Type III Polyketide synthases (PKS) are family of proteins considered to have significant roles in the biosynthesis of various polyketides in plants, fungi and bacteria. As these proteins shows positive effects to human health, more researches are going on regarding this particular protein. Developing a tool to identify the probability of sequence being a type III polyketide synthase will minimize the time consumption and manpower efforts. In this approach, we have designed and implemented PKSIIIpred, a high performance prediction server for type III PKS where the classifier is Support Vector Machines (SVMs). Based on the limited training dataset, the tool efficiently predicts the type III PKS superfamily of proteins with high sensitivity and specificity. The PKSIIIpred is available at http://type3pks.in/prediction/. We expect that this tool may serve as a useful resource for type III PKS researchers. Currently work is being progressed for further betterment of prediction accuracy by including more sequence features in the training dataset.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1613-4516
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
7
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Kernel based machine learning algorithm for the efficient prediction of type III polyketide synthase family of proteins.
pubmed:affiliation
Plant Molecular Biology Division, Rajiv Gandhi Centre for Biotechnology, Thycaud P O, Poojappura, Thiruvananthapuram, 695 014, Kerala, India. mallika@rgdb.res.in
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't