Med Biol Eng Comput - The influence of alignment-free sequence representations on the semi-supervised classification of class C G protein-coupled receptors: semi-supervised classification of class C GPCRs.

Tópicos

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{ imag(1947) propos(1133) code(1026) }
{ framework(1458) process(801) describ(734) }
{ learn(2355) train(1041) set(1003) }
{ howev(809) still(633) remain(590) }
{ can(774) often(719) complex(702) }
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{ chang(1828) time(1643) increas(1301) }
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{ risk(3053) factor(974) diseas(938) }
{ data(2317) use(1299) case(1017) }
{ data(3008) multipl(1320) sourc(1022) }
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Resumo

G protein-coupled receptors (GPCRs) are integral cell membrane proteins of relevance for pharmacology. The tertiary structure of the transmembrane domain, a gate to the study of protein functionality, is unknown for almost all members of class C GPCRs, which are the target of the current study. As a result, their investigation must often rely on alignments of their amino acid sequences. Sequence alignment entails the risk of missing relevant information. Various approaches have attempted to circumvent this risk through alignment-free transformations of the sequences on the basis of different amino acid physicochemical properties. In this paper, we use several of these alignment-free methods, as well as a basic amino acid composition representation, to transform the available sequences. Novel semi-supervised statistical machine learning methods are then used to discriminate the different class C GPCRs types from the transformed data. This approach is relevant due to the existence of orphan proteins to which type labels should be assigned in a process of deorphanization or reverse pharmacology. The reported experiments show that the proposed techniques provide accurate classification even in settings of extreme class-label scarcity and that fair accuracy can be achieved even with very simple transformation strategies that ignore the sequence ordering.

Resumo Limpo

g proteincoupl receptor gpcrs integr cell membran protein relev pharmacolog tertiari structur transmembran domain gate studi protein function unknown almost member class c gpcrs target current studi result investig must often reli align amino acid sequenc sequenc align entail risk miss relev inform various approach attempt circumv risk alignmentfre transform sequenc basi differ amino acid physicochem properti paper use sever alignmentfre method well basic amino acid composit represent transform avail sequenc novel semisupervis statist machin learn method use discrimin differ class c gpcrs type transform data approach relev due exist orphan protein type label assign process deorphan revers pharmacolog report experi show propos techniqu provid accur classif even set extrem classlabel scarciti fair accuraci can achiev even simpl transform strategi ignor sequenc order

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