Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2008-6-27
pubmed:abstractText
Transcription factors (TFs) are core functional proteins which play important roles in gene expression control, and they are key factors for gene regulation network construction. Traditionally, they were identified and classified through experimental approaches. In order to save time and reduce costs, many computational methods have been developed to identify TFs from new proteins and to classify the resulted TFs. Though these methods have facilitated screening of TFs to some extent, low accuracy is still a common problem. With the fast growing number of new proteins, more precise algorithms for identifying TFs from new proteins and classifying the consequent TFs are in a high demand.
pubmed:commentsCorrections
http://linkedlifedata.com/resource/pubmed/commentcorrection/18554421-10390613, http://linkedlifedata.com/resource/pubmed/commentcorrection/18554421-10592257, http://linkedlifedata.com/resource/pubmed/commentcorrection/18554421-11301304, http://linkedlifedata.com/resource/pubmed/commentcorrection/18554421-11473021, http://linkedlifedata.com/resource/pubmed/commentcorrection/18554421-12079362, http://linkedlifedata.com/resource/pubmed/commentcorrection/18554421-12520026, http://linkedlifedata.com/resource/pubmed/commentcorrection/18554421-12522256, http://linkedlifedata.com/resource/pubmed/commentcorrection/18554421-12912846, http://linkedlifedata.com/resource/pubmed/commentcorrection/18554421-1497306, http://linkedlifedata.com/resource/pubmed/commentcorrection/18554421-15358128, http://linkedlifedata.com/resource/pubmed/commentcorrection/18554421-15510160, http://linkedlifedata.com/resource/pubmed/commentcorrection/18554421-15706536, http://linkedlifedata.com/resource/pubmed/commentcorrection/18554421-15731212, http://linkedlifedata.com/resource/pubmed/commentcorrection/18554421-16381825, http://linkedlifedata.com/resource/pubmed/commentcorrection/18554421-16584572, http://linkedlifedata.com/resource/pubmed/commentcorrection/18554421-16731699, http://linkedlifedata.com/resource/pubmed/commentcorrection/18554421-16769687, http://linkedlifedata.com/resource/pubmed/commentcorrection/18554421-16808896, http://linkedlifedata.com/resource/pubmed/commentcorrection/18554421-17142230, http://linkedlifedata.com/resource/pubmed/commentcorrection/18554421-17202162, http://linkedlifedata.com/resource/pubmed/commentcorrection/18554421-17237068, http://linkedlifedata.com/resource/pubmed/commentcorrection/18554421-17428441, http://linkedlifedata.com/resource/pubmed/commentcorrection/18554421-18042272, http://linkedlifedata.com/resource/pubmed/commentcorrection/18554421-18325330, http://linkedlifedata.com/resource/pubmed/commentcorrection/18554421-7566094, http://linkedlifedata.com/resource/pubmed/commentcorrection/18554421-9685261, http://linkedlifedata.com/resource/pubmed/commentcorrection/18554421-9727051
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1471-2105
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
9
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
282
pubmed:dateRevised
2009-11-18
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
The combination approach of SVM and ECOC for powerful identification and classification of transcription factor.
pubmed:affiliation
Department of Computing and Information Technology, Fudan University, 220 Handan Road, Shanghai 200433, PR China. zhenggy@sibs.ac.cn
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't