Comput Biol Chem - Derivation of an artificial gene to improve classification accuracy upon gene selection.

Tópicos

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{ gene(2352) biolog(1181) express(1162) }
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{ age(1611) year(1155) adult(843) }
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{ first(2504) two(1366) second(1323) }
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{ structur(1116) can(940) graph(676) }
{ cancer(2502) breast(956) screen(824) }
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{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ survey(1388) particip(1329) question(1065) }
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{ activ(1452) weight(1219) physic(1104) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Classification analysis has been developed continuously since 1936. This research field has advanced as a result of development of classifiers such as KNN, ANN, and SVM, as well as through data preprocessing areas. Feature (gene) selection is required for very high dimensional data such as microarray before classification work. The goal of feature selection is to choose a subset of informative features that reduces processing time and provides higher classification accuracy. In this study, we devised a method of artificial gene making (AGM) for microarray data to improve classification accuracy. Our artificial gene was derived from a whole microarray dataset, and combined with a result of gene selection for classification analysis. We experimentally confirmed a clear improvement of classification accuracy after inserting artificial gene. Our artificial gene worked well for popular feature (gene) selection algorithms and classifiers. The proposed approach can be applied to any type of high dimensional dataset.

Resumo Limpo

classif analysi develop continu sinc research field advanc result develop classifi knn ann svm well data preprocess area featur gene select requir high dimension data microarray classif work goal featur select choos subset inform featur reduc process time provid higher classif accuraci studi devis method artifici gene make agm microarray data improv classif accuraci artifici gene deriv whole microarray dataset combin result gene select classif analysi experiment confirm clear improv classif accuraci insert artifici gene artifici gene work well popular featur gene select algorithm classifi propos approach can appli type high dimension dataset

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