Open AccessDeep learning‐based microarray cancer classification and ensemble gene selection approach
Author(s) -
Rezaee Khosro,
Jeon Gwanggil,
Khosravi Mohammad R.,
Attar Hani H.,
Sabzevari Alireza
Publication year - 2022
Publication title -
iet systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.367
H-Index - 50
eISSN - 1751-8857
pISSN - 1751-8849
DOI - 10.1049/syb2.12044
Subject(s) - microarray , microarray analysis techniques , artificial intelligence , feature selection , computer science , dna microarray , artificial neural network , computational biology , deep learning , selection (genetic algorithm) , pattern recognition (psychology) , gene , data mining , machine learning , biology , gene expression , genetics
Malignancies and diseases of various genetic origins can be diagnosed and classified with microarray data. There are many obstacles to overcome due to the large size of the gene and the small number of samples in the microarray. A combination strategy for gene expression in a variety of diseases is described in this paper, consisting of two steps: identifying the most effective genes via soft ensembling and classifying them with a novel deep neural network. The feature selection approach combines three strategies to select wrapper genes and rank them according to the k‐nearest neighbour algorithm, resulting in a very generalisable model with low error levels. Using soft ensembling, the most effective subsets of genes were identified from three microarray datasets of diffuse large cell lymphoma, leukaemia, and prostate cancer. A stacked deep neural network was used to classify all three datasets, achieving an average accuracy of 97.51%, 99.6%, and 96.34%, respectively. In addition, two previously unreported datasets from small, round blue cell tumors (SRBCTs)and multiple sclerosis‐related brain tissue lesions were examined to show the generalisability of the model method.