Faria Alexandre Wagner Chagas, da Silva Alisson Marques, de Souza Rodrigues Thiago, Costa Marcelo Azevedo, Braga Antonio Padua
1 Graduate Program in Electrical Engineering, Federal University of Minas Gerais , Belo Horizonte, MG, Brazil .
3 Federal Center of Technological Education of Minas Gerais , Divinópolis, MG, Brazil .
J Comput Biol. 2015 Oct;22(10):953-61. doi: 10.1089/cmb.2013.0125.
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.
急性白血病分为髓系和淋巴细胞系亚型通常是根据肿瘤的形态来完成的。然而，这些亚型可能具有相似的组织病理学外观，使得筛查程序变得困难。此外，大约三分之一的急性髓系白血病的特征是核磷蛋白异常定位于细胞质中（NPMc(+)），其中大多数具有正常核型。这项工作基于两个公开可用的DNA微阵列数据集来区分白血病亚型。将数据集分为训练集和测试集，并应用了特征选择方法。开发了人工神经网络分类器来比较特征选择方法。对于第一个数据集，使用最佳分类器选择的50个基因能够对测试集中的所有患者进行分类。对于第二个数据集，五个基因在测试集中的准确率达到了97.5%。
