A novel artificial intelligence approach to detect the breast cancer using KNNet technique with EPM gene profiling.
Funct Integr Genomics
; 23(4): 302, 2023 Sep 18.
Article
em En
| MEDLINE
| ID: mdl-37721631
ABSTRACT
Women's most frequent type of cancer is breast cancer, second only to lung cancer. This paper summarizes changes in genomics and epigenetics and incremental biological activities. A tumour develops through a series of phases involving a separate abnormal gene. Even though many diseases cause DNA mutations, most treatments are designed to relieve symptoms rather than change the DNA. Clustering short palindromic repeats (CRISPR) or Cas9 is the primary approach for discovering and confirming tumorigenic genomic targets. A Kohonen neural network with an expression programming model was developed for gene selection. The main problem in genetic selection is reducing the number of features chosen while maintaining accuracy. This purpose is accomplished systematically. In the end, the approach method performed better than the existing quantum squirrel-inspired algorithm and the recurrent neural network oppositional call search algorithm for genetic selection. The KNNet-EPM model used an expression programming approach to identify gene biomarkers for breast cancer. This method was achieved with RAE of 42%, sensitivity of 93%, f1 score of 88%, accuracy of 98%, kappa score of 83%, specificity of 92% and MAE of 30%.
Palavras-chave
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Neoplasias da Mama
/
Neoplasias Pulmonares
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Female
/
Humans
Idioma:
En
Revista:
Funct Integr Genomics
Assunto da revista:
BIOLOGIA MOLECULAR
/
GENETICA
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Índia