In Silico Identification of Effective Genes for Acute Leukemia Classification Using a Spline Regression-based Framework

نویسندگان
Yazdanparast, Maryam ID 1
Sheikhpour, Razieh ID 2
Ghanizadeh, Fatemeh ID 3
Zangeneh Soroush, Morteza ID 4

1 Department of pediatrics, Shahid Sadoughi hospital, Shahid Sadoughi University, Yazd, Iran

2 Department of Computer Engineering, Faculty of Engineering, Ardakan University, P.O. Box 184, Ardakan, Iran

3 Hematology and Oncology Research Center, Shahid Sadoughi University of Medical Sciences, Yazd, Iran

4 Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

doi
10.18502/ijpho.v14i2.15269
چکیده

Background: Microarray technology enables the examination of gene expression in thousands of genes and can be highly effective in identifying various types of cancers, including leukemia. However, many genes in microarray data are redundant and lack useful information for cancer diagnosis. The main objective of this study is to identify ...

کلیدواژه‌ها
Acute lymphocytic leukemia, Acute myeloid leukemia, Gene expression, Sparse gene selection, Spline regression