GSEnet: feature extraction of gene expression data and its application to Leukemia classification.
Math Biosci Eng
; 19(5): 4881-4891, 2022 03 14.
Article
en En
| MEDLINE
| ID: mdl-35430845
ABSTRACT
Gene expression data is highly dimensional. As disease-related genes account for only a tiny fraction, a deep learning model, namely GSEnet, is proposed to extract instructive features from gene expression data. This model consists of three modules, namely the pre-conv module, the SE-Resnet module, and the SE-conv module. Effectiveness of the proposed model on the performance improvement of 9 representative classifiers is evaluated. Seven evaluation metrics are used for this assessment on the GSE99095 dataset. Robustness and advantages of the proposed model compared with representative feature selection methods are also discussed. Results show superiority of the proposed model on the improvement of the classification precision and accuracy.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Leucemia
/
Redes Neurales de la Computación
Límite:
Humans
Idioma:
En
Revista:
Math Biosci Eng
Año:
2022
Tipo del documento:
Article
País de afiliación:
China