J Chem Inf Model - Classifier ensemble based on feature selection and diversity measures for predicting the affinity of A(2B) adenosine receptor antagonists.

Tópicos

{ featur(3375) classif(2383) classifi(1994) }
{ model(2656) set(1616) predict(1553) }
{ bind(1733) structur(1185) ligand(1036) }
{ data(2317) use(1299) case(1017) }
{ method(2212) result(1239) propos(1039) }
{ perform(1367) use(1326) method(1137) }
{ structur(1116) can(940) graph(676) }
{ learn(2355) train(1041) set(1003) }
{ error(1145) method(1030) estim(1020) }
{ perform(999) metric(946) measur(919) }
{ network(2748) neural(1063) input(814) }
{ use(1733) differ(960) four(931) }
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{ patient(2315) diseas(1263) diabet(1191) }
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{ drug(1928) target(777) effect(648) }
{ cancer(2502) breast(956) screen(824) }
{ health(1844) social(1437) communiti(874) }
{ analysi(2126) use(1163) compon(1037) }
{ activ(1138) subject(705) human(624) }
{ age(1611) year(1155) adult(843) }
{ state(1844) use(1261) util(961) }
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{ visual(1396) interact(850) tool(830) }
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{ sequenc(1873) structur(1644) protein(1328) }
{ imag(1057) registr(996) error(939) }
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{ imag(1947) propos(1133) code(1026) }
{ can(774) often(719) complex(702) }

Resumo

A(2B) adenosine receptor antagonists may be beneficial in treating diseases like asthma, diabetes, diabetic retinopathy, and certain cancers. This has stimulated research for the development of potent ligands for this subtype, based on quantitative structure-affinity relationships. In this work, a new ensemble machine learning algorithm is proposed for classification and prediction of the ligand-binding affinity of A(2B) adenosine receptor antagonists. This algorithm is based on the training of different classifier models with multiple training sets (composed of the same compounds but represented by diverse features). The k-nearest neighbor, decision trees, neural networks, and support vector machines were used as single classifiers. To select the base classifiers for combining into the ensemble, several diversity measures were employed. The final multiclassifier prediction results were computed from the output obtained by using a combination of selected base classifiers output, by utilizing different mathematical functions including the following: majority vote, maximum and average probability. In this work, 10-fold cross- and external validation were used. The strategy led to the following results: i) the single classifiers, together with previous features selections, resulted in good overall accuracy, ii) a comparison between single classifiers, and their combinations in the multiclassifier model, showed that using our ensemble gave a better performance than the single classifier model, and iii) our multiclassifier model performed better than the most widely used multiclassifier models in the literature. The results and statistical analysis demonstrated the supremacy of our multiclassifier approach for predicting the affinity of A(2B) adenosine receptor antagonists, and it can be used to develop other QSAR models.

Resumo Limpo

ab adenosin receptor antagonist may benefici treat diseas like asthma diabet diabet retinopathi certain cancer stimul research develop potent ligand subtyp base quantit structureaffin relationship work new ensembl machin learn algorithm propos classif predict ligandbind affin ab adenosin receptor antagonist algorithm base train differ classifi model multipl train set compos compound repres divers featur knearest neighbor decis tree neural network support vector machin use singl classifi select base classifi combin ensembl sever divers measur employ final multiclassifi predict result comput output obtain use combin select base classifi output util differ mathemat function includ follow major vote maximum averag probabl work fold cross extern valid use strategi led follow result singl classifi togeth previous featur select result good overal accuraci ii comparison singl classifi combin multiclassifi model show use ensembl gave better perform singl classifi model iii multiclassifi model perform better wide use multiclassifi model literatur result statist analysi demonstr supremaci multiclassifi approach predict affin ab adenosin receptor antagonist can use develop qsar model

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