Comput. Biol. Med. - A novel class dependent feature selection method for cancer biomarker discovery.

Tópicos

{ featur(3375) classif(2383) classifi(1994) }
{ cancer(2502) breast(956) screen(824) }
{ gene(2352) biolog(1181) express(1162) }
{ perform(999) metric(946) measur(919) }
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Resumo

Identifying key biomarkers for different cancer types can improve diagnosis accuracy and treatment. Gene expression data can help differentiate between cancer subtypes. However the limitation of having a small number of samples versus a larger number of genes represented in a dataset leads to the overfitting of classification models. Feature selection methods can help select the most distinguishing feature sets for classifying different cancers. A new class dependent feature selection approach integrates the F-statistic, Maximum Relevance Binary Particle Swarm Optimization (MRBPSO) and Class Dependent Multi-category Classification (CDMC) system. This feature selection method combines filter and wrapper based methods. A set of highly differentially expressed genes (features) are pre-selected using the F statistic for each dataset as a filter for selecting the most meaningful features. MRBPSO and CDMC function as a wrapper to select desirable feature subsets for each class and classify the samples using those chosen class-dependent feature subsets. The performance of the proposed methods is evaluated on eight real cancer datasets. The results indicate that the class-dependent approaches can effectively identify biomarkers related to each cancer type and improve classification accuracy compared to class independent feature selection methods.

Resumo Limpo

identifi key biomark differ cancer type can improv diagnosi accuraci treatment gene express data can help differenti cancer subtyp howev limit small number sampl versus larger number gene repres dataset lead overfit classif model featur select method can help select distinguish featur set classifi differ cancer new class depend featur select approach integr fstatist maximum relev binari particl swarm optim mrbpso class depend multicategori classif cdmc system featur select method combin filter wrapper base method set high differenti express gene featur preselect use f statist dataset filter select meaning featur mrbpso cdmc function wrapper select desir featur subset class classifi sampl use chosen classdepend featur subset perform propos method evalu eight real cancer dataset result indic classdepend approach can effect identifi biomark relat cancer type improv classif accuraci compar class independ featur select method

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