Comput. Biol. Med. - In silico prediction of spleen tyrosine kinase inhibitors using machine learning approaches and an optimized molecular descriptor subset generated by recursive feature elimination method.

Tópicos

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{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
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{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
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{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
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{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

We tested four machine learning methods, support vector machine (SVM), k-nearest neighbor, back-propagation neural network and C4.5 decision tree for their capability in predicting spleen tyrosine kinase (Syk) inhibitors by using 2592 compounds which are more diverse than those in other studies. The recursive feature elimination method was used for improving prediction performance and selecting molecular descriptors responsible for distinguishing Syk inhibitors and non-inhibitors. Among four machine learning models, SVM produces the best performance at 99.18% for inhibitors and 98.82% for non-inhibitors, respectively, indicating that the SVM is potentially useful for facilitating the discovery of Syk inhibitors.

Resumo Limpo

test four machin learn method support vector machin svm knearest neighbor backpropag neural network c decis tree capabl predict spleen tyrosin kinas syk inhibitor use compound divers studi recurs featur elimin method use improv predict perform select molecular descriptor respons distinguish syk inhibitor noninhibitor among four machin learn model svm produc best perform inhibitor noninhibitor respect indic svm potenti use facilit discoveri syk inhibitor

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