J Integr Bioinform - Using variable precision rough set for selection and classification of biological knowledge integrated in DNA gene expression.

Tópicos

{ gene(2352) biolog(1181) express(1162) }
{ model(2656) set(1616) predict(1553) }
{ system(1976) rule(880) can(841) }
{ method(1969) cluster(1462) data(1082) }
{ learn(2355) train(1041) set(1003) }
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{ imag(2830) propos(1344) filter(1198) }
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{ medic(1828) order(1363) alert(1069) }
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{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

DNA microarrays have contributed to the exponential growth of genomic and experimental data in the last decade. This large amount of gene expression data has been used by researchers seeking diagnosis of diseases like cancer using machine learning methods. In turn, explicit biological knowledge about gene functions has also grown tremendously over the last decade. This work integrates explicit biological knowledge, provided as gene sets, into the classication process by means of Variable Precision Rough Set Theory (VPRS). The proposed model is able to highlight which part of the provided biological knowledge has been important for classification. This paper presents a novel model for microarray data classification which is able to incorporate prior biological knowledge in the form of gene sets. Based on this knowledge, we transform the input microarray data into supergenes, and then we apply rough set theory to select the most promising supergenes and to derive a set of easy interpretable classification rules. The proposed model is evaluated over three breast cancer microarrays datasets obtaining successful results compared to classical classification techniques. The experimental results shows that there are not significant differences between our model and classical techniques but it is able to provide a biological-interpretable explanation of how it classifies new samples.

Resumo Limpo

dna microarray contribut exponenti growth genom experiment data last decad larg amount gene express data use research seek diagnosi diseas like cancer use machin learn method turn explicit biolog knowledg gene function also grown tremend last decad work integr explicit biolog knowledg provid gene set classic process mean variabl precis rough set theori vprs propos model abl highlight part provid biolog knowledg import classif paper present novel model microarray data classif abl incorpor prior biolog knowledg form gene set base knowledg transform input microarray data supergen appli rough set theori select promis supergen deriv set easi interpret classif rule propos model evalu three breast cancer microarray dataset obtain success result compar classic classif techniqu experiment result show signific differ model classic techniqu abl provid biologicalinterpret explan classifi new sampl

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