A Hybrid Machine Learning Approach to Screen Optimal Predictors for the Classification of Primary Breast Tumors from Gene Expression Microarray Data

The high dimensionality and sparsity of the microarray gene expression data make it challenging to analyze and screen the optimal subset of genes as predictors of breast cancer (BC). The authors in the present study propose a novel hybrid Feature Selection (FS) sequential framework involving minimum...

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Detalles Bibliográficos
Autores principales: Alromema, Nashwan, Syed, Asif Hassan, Khan, Tabrej
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9955903/
https://www.ncbi.nlm.nih.gov/pubmed/36832196
http://dx.doi.org/10.3390/diagnostics13040708

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9955903/
https://www.ncbi.nlm.nih.gov/pubmed/36832196
http://dx.doi.org/10.3390/diagnostics13040708

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