Comput. Biol. Med. - Robust prediction of protein subcellular localization combining PCA and WSVMs.

Tópicos

{ learn(2355) train(1041) set(1003) }
{ featur(3375) classif(2383) classifi(1994) }
{ drug(1928) target(777) effect(648) }
{ imag(2830) propos(1344) filter(1198) }
{ perform(999) metric(946) measur(919) }
{ process(1125) use(805) approach(778) }
{ method(2212) result(1239) propos(1039) }
{ imag(1057) registr(996) error(939) }
{ howev(809) still(633) remain(590) }
{ studi(1119) effect(1106) posit(819) }
{ data(3008) multipl(1320) sourc(1022) }
{ activ(1452) weight(1219) physic(1104) }
{ can(774) often(719) complex(702) }
{ system(1976) rule(880) can(841) }
{ sequenc(1873) structur(1644) protein(1328) }
{ imag(2675) segment(2577) method(1081) }
{ extract(1171) text(1153) clinic(932) }
{ control(1307) perform(991) simul(935) }
{ search(2224) databas(1162) retriev(909) }
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{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
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{ can(981) present(881) function(850) }
{ structur(1116) can(940) graph(676) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Automated prediction of protein subcellular localization is an important tool for genome annotation and drug discovery, and Support Vector Machines (SVMs) can effectively solve this problem in a supervised manner. However, the datasets obtained from real experiments are likely to contain outliers or noises, which can lead to poor generalization ability and classification accuracy. To explore this problem, we adopt strategies to lower the effect of outliers. First we design a method based on Weighted SVMs, different weights are assigned to different data points, so the training algorithm will learn the decision boundary according to the relative importance of the data points. Second we analyse the influence of Principal Component Analysis (PCA) on WSVM classification, propose a hybrid classifier combining merits of both PCA and WSVM. After performing dimension reduction operations on the datasets, kernel-based possibilistic c-means algorithm can generate more suitable weights for the training, as PCA transforms the data into a new coordinate system with largest variances affected greatly by the outliers. Experiments on benchmark datasets show promising results, which confirms the effectiveness of the proposed method in terms of prediction accuracy.

Resumo Limpo

autom predict protein subcellular local import tool genom annot drug discoveri support vector machin svms can effect solv problem supervis manner howev dataset obtain real experi like contain outlier nois can lead poor general abil classif accuraci explor problem adopt strategi lower effect outlier first design method base weight svms differ weight assign differ data point train algorithm will learn decis boundari accord relat import data point second analys influenc princip compon analysi pca wsvm classif propos hybrid classifi combin merit pca wsvm perform dimens reduct oper dataset kernelbas possibilist cmean algorithm can generat suitabl weight train pca transform data new coordin system largest varianc affect great outlier experi benchmark dataset show promis result confirm effect propos method term predict accuraci

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