title: A Ranking Approach for Probe Selection and Classification of Microarray Data with Artificial Neural Networks.
abstract: Acute leukemia classification into its myeloid and lymphoblastic subtypes is usually accomplished according to the morphology of the tumor. Nevertheless, the subtypes may have similar histopathological appearance, making screening procedures difficult. In addition, approximately one-third of acute myeloid leukemias are characterized by aberrant cytoplasmic localization of nucleophosmin (NPMc(+)), where the majority has a normal karyotype. This work is based on two DNA microarray datasets, available publicly, to differentiate leukemia subtypes. The datasets were split into training and test sets, and feature selection methods were applied. Artificial neural network classifiers were developed to compare the feature selection methods. For the first dataset, 50 genes selected using the best classifier was able to classify all patients in the test set. For the second dataset, five genes yielded 97.5% accuracy in the test set.
authors.affiliation: 1 Graduate Program in Electrical Engineering, Federal University of Minas Gerais , Belo Horizonte, MG, Brazil .
authors.forename: Alexandre Wagner Chagas
authors.initials: AW
authors.lastname: Faria
authors.affiliation: 3 Federal Center of Technological Education of Minas Gerais , Divinópolis, MG, Brazil .
authors.forename: Alisson Marques
authors.initials: AM
authors.lastname: da Silva
authors.affiliation: 2 Computer Department, Federal Center of Technological Education of Minas Gerais , Belo Horizonte, MG, Brazil .
authors.forename: Thiago
authors.initials: T
authors.lastname: de Souza Rodrigues
authors.affiliation: 1 Graduate Program in Electrical Engineering, Federal University of Minas Gerais , Belo Horizonte, MG, Brazil .
authors.forename: Marcelo Azevedo
authors.initials: MA
authors.lastname: Costa
authors.affiliation: 1 Graduate Program in Electrical Engineering, Federal University of Minas Gerais , Belo Horizonte, MG, Brazil .
authors.forename: Antonio Padua
authors.initials: AP
authors.lastname: Braga
mesh_terms: D000465:Algorithms; D003937:Diagnosis, Differential; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D007951:Leukemia, Myeloid / Q000145:classification / Q000235:genetics*; D016571:Neural Networks, Computer; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000145:classification / Q000235:genetics*; D012680:Sensitivity and Specificity
keywords: gene expression profile; machine learning