Open AccessFeature selection and classification approaches in gene expression of breast cancer
Author(s) -
Sarada Ghosh,
AUTHOR_ID,
G. P. Samanta,
M. De La Sen,
AUTHOR_ID,
AUTHOR_ID
Publication year - 2021
Publication title -
aims biophysics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.545
H-Index - 12
ISSN - 2377-9098
DOI - 10.3934/biophy.2021029
Subject(s) - random forest , feature selection , dimensionality reduction , principal component analysis , classifier (uml) , decision tree , breast cancer , computer science , microarray analysis techniques , microarray , artificial intelligence , computational biology , gene , data mining , pattern recognition (psychology) , biology , cancer , gene expression , genetics
DNA microarray technology with biological data-set can monitor the expression levels of thousands of genes simultaneously. Microarray data analysis is important in phenotype classification of diseases. In this work, the computational part basically predicts the tendency towards mortality using different classification techniques by identifying features from the high dimensional dataset. We have analyzed the breast cancer transcriptional genomic data of 1554 transcripts captured over from 272 samples. This work presents effective methods for gene classification using Logistic Regression (LR), Random Forest (RF), Decision Tree (DT) and constructs a classifier with an upgraded rate of accuracy than all features together. The performance of these underlying methods are also compared with dimension reduction method, namely, Principal Component Analysis (PCA). The methods of feature reduction with RF, LR and decision tree (DT) provide better performance than PCA. It is observed that both techniques LR and RF identify TYMP, ERS1, C-MYB and TUBA1a genes. But some features corresponding to the genes such as ARID4B, DNMT3A, TOX3, RGS17 and PNLIP are uniquely pointed out by LR method which are leading to a significant role in breast cancer. The simulation is based on R -software.