Comput. Biol. Med. - ProClusEnsem: predicting membrane protein types by fusing different modes of pseudo amino acid composition.

Tópicos

{ sequenc(1873) structur(1644) protein(1328) }
{ featur(1941) imag(1645) propos(1176) }
{ learn(2355) train(1041) set(1003) }
{ perform(999) metric(946) measur(919) }
{ algorithm(1844) comput(1787) effici(935) }
{ studi(1410) differ(1259) use(1210) }
{ featur(3375) classif(2383) classifi(1994) }
{ use(976) code(926) identifi(902) }
{ imag(2830) propos(1344) filter(1198) }
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{ model(3404) distribut(989) bayesian(671) }
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{ health(1844) social(1437) communiti(874) }
{ inform(2794) health(2639) internet(1427) }
{ measur(2081) correl(1212) valu(896) }
{ bind(1733) structur(1185) ligand(1036) }
{ assess(1506) score(1403) qualiti(1306) }
{ framework(1458) process(801) describ(734) }
{ control(1307) perform(991) simul(935) }
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{ method(2212) result(1239) propos(1039) }

Resumo

Knowing the type of an uncharacterized membrane protein often provides a useful clue in both basic research and drug discovery. With the explosion of protein sequences generated in the post genomic era, determination of membrane protein types by experimental methods is expensive and time consuming. It therefore becomes important to develop an automated method to find the possible types of membrane proteins. In view of this, various computational membrane protein prediction methods have been proposed. They extract protein feature vectors, such as PseAAC (pseudo amino acid composition) and PsePSSM (pseudo position-specific scoring matrix) for representation of protein sequence, and then learn a distance metric for the KNN (K nearest neighbor) or NN (nearest neighbor) classifier to predicate the final type. Most of the metrics are learned using linear dimensionality reduction algorithms like Principle Components Analysis (PCA) and Linear Discriminant Analysis (LDA). Such metrics are common to all the proteins in the dataset. In fact, they assume that the proteins lie on a uniform distribution, which can be captured by the linear dimensionality reduction algorithm. We doubt this assumption, and learn local metrics which are optimized for local subset of the whole proteins. The learning procedure is iterated with the protein clustering. Then a novel ensemble distance metric is given by combining the local metrics through Tikhonov regularization. The experimental results on a benchmark dataset demonstrate the feasibility and effectiveness of the proposed algorithm named ProClusEnsem.

Resumo Limpo

know type uncharacter membran protein often provid use clue basic research drug discoveri explos protein sequenc generat post genom era determin membran protein type experiment method expens time consum therefor becom import develop autom method find possibl type membran protein view various comput membran protein predict method propos extract protein featur vector pseaac pseudo amino acid composit psepssm pseudo positionspecif score matrix represent protein sequenc learn distanc metric knn k nearest neighbor nn nearest neighbor classifi predic final type metric learn use linear dimension reduct algorithm like principl compon analysi pca linear discrimin analysi lda metric common protein dataset fact assum protein lie uniform distribut can captur linear dimension reduct algorithm doubt assumpt learn local metric optim local subset whole protein learn procedur iter protein cluster novel ensembl distanc metric given combin local metric tikhonov regular experiment result benchmark dataset demonstr feasibl effect propos algorithm name proclusensem

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