Linked Life Data

20394581

Source:http://linkedlifedata.com/resource/pubmed/id/20394581

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
7
pubmed:dateCreated
2010-6-30
pubmed:abstractText
The transcription factor (TF) is a protein that binds DNA at specific site to help regulate the transcription from DNA to RNA. The mechanism of transcriptional regulatory can be much better understood if the category of transcription factors is known. We introduce a system which can automatically categorize transcription factors using their primary structures. A feature analysis strategy called "mRMR" (Minimum Redundancy, Maximum Relevance) is used to analyze the contribution of the TF properties towards the TF classification. mRMR is coupled with forward feature selection to choose an optimized feature subset for the classification. TF properties are composed of the amino acid composition and the physiochemical characters of the proteins. These properties will generate over a hundred features/parameters. We put all the features/parameters into a classifier, called NNA (nearest neighbor algorithm), for the classification. The classification accuracy is 93.81%, evaluated by a Jackknife test. Feature analysis using mRMR algorithm shows that secondary structure, amino acid composition and hydrophobicity are the most relevant features for classification. A free online classifier is available at http://app3.biosino.org/132dvc/tf/.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
1875-5305
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
17
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
899-908
pubmed:dateRevised
2010-11-18
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Classification of transcription factors using protein primary structure.
pubmed:affiliation
CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, China.
pubmed:publicationType
Journal Article