Machine learning methods applied to DNA microarray data can improve the diagnosis of cancer

2003 ◽  
Vol 5 (2) ◽  
pp. 48-55 ◽  
Author(s):  
Eric Bair ◽  
Robert Tibshirani
Keyword(s):  
Machine Learning ◽  
Dna Microarray ◽  
Microarray Data ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Dna Microarray Data ◽  
Diagnosis Of Cancer

Gene Identification from Microarray Data for Diagnosis of Acute Myeloid and Lymphoblastic Leukemia Using a Sparse Gene Selection Method

2021 ◽  
Author(s):  
Razieh Sheikhpour ◽  
Roohallah Fazli ◽  
Sanaz Mehrabani
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Microarray Data ◽  
Lymphoblastic Leukemia ◽  
Feature Selection Method ◽  
Selection Method ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Acute Myeloid ◽  
Sparse Feature Selection

Background: Microarray experiments can simultaneously determine the expression of thousands of genes. Identification of potential genes from microarray data for diagnosis of cancer is important. This study aimed to identify genes for the diagnosis of acute myeloid and lymphoblastic leukemia using a sparse feature selection method. Materials and Methods: In this descriptive study, the expression of 7129 genes of 25 patients with acute myeloid leukemia (AML), and 47 patients with lymphoblastic leukemia (ALL) achieved by the microarray technology were used in this study. Then, the important genes were identified using a sparse feature selection method to diagnose AML and ALL tissues based on the machine learning methods such as support vector machine (SVM), Gaussian kernel density estimation based classifier (GKDEC), k-nearest neighbor (KNN), and linear discriminant classifier (LDC). Results: Diagnosis of ALL and AML was done with the accuracy of 100% using 8 genes of microarray data selected by the sparse feature selection method, GKDEC, and LDC. Moreover, the KNN classifier using 6 genes and the SVM classifier using 7 genes diagnosed AML and ALL with the accuracy of 91.18% and 94.12%, respectively. The gene with the description “Paired-box protein PAX2 (PAX2) gene, exon 11 and complete CDs” was determined as the most important gene in the diagnosis of ALL and AML. Conclusion: The experimental results of the current study showed that AML and ALL can be diagnosed with high accuracy using sparse feature selection and machine learning methods. It seems that the investigation of the expression of selected genes in this study can be helpful in the diagnosis of ALL and AML.

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