Syntactic Approach to Predict Membrane Spanning Regions of Transmembrane Proteins

Publication:
Syntactic Approach to Predict Membrane Spanning Regions of Transmembrane Proteins

dc.contributor.author	Pulasinghe, K
dc.contributor.author	Rajapakse, J. C
dc.date.accessioned	2022-01-28T04:07:55Z
dc.date.available	2022-01-28T04:07:55Z
dc.date.issued	2005-03-30
dc.description.abstract	This paper exploits “biological grammar” of transmembrane proteins to predict their membrane spanning regions using hidden Markov models and elaborates a set of syntactic rules to model the distinct features of transmembrane proteins. This paves the way to identify the characteristics of membrane proteins analogous to the way that identifies language contents of speech utterances by using hidden Markov models. The proposed method correctly predicts 95.24% of the membrane spanning regions of the known transmembrane proteins and correctly predicts 79.87% of the membrane spanning regions of the unknown transmembrane proteins on a benchmark dataset.	en_US
dc.identifier.isbn	978-3-540-25396-9
dc.identifier.uri	https://rda.sliit.lk/handle/123456789/804
dc.language.iso	en	en_US
dc.publisher	Springer, Berlin, Heidelberg	en_US
dc.relation.ispartofseries	Workshops on Applications of Evolutionary Computation;Pages 95-104
dc.subject	Hide Markov Model	en_US
dc.subject	Transmembrane Protein	en_US
dc.subject	Syntactic Rule	en_US
dc.subject	Hide Markov Model Model	en_US
dc.subject	Syntactic Approach	en_US
dc.title	Syntactic Approach to Predict Membrane Spanning Regions of Transmembrane Proteins	en_US
dc.type	Article	en_US
dspace.entity.type	Publication