SMAGS-LASSO: A Novel Feature Selection Method for Sensitivity Maximization in Early Cancer Detection
This method provides a promising approach for early cancer detection and other medical diagnostics requiring sensitivity-specificity optimization.
Authors
- Ehsan Irajizad (ORCID: https://orcid.org/0000-0001-7510-4849)
- Hamid Khoshfekr Rudsari (ORCID: https://orcid.org/0000-0002-7856-0038)
- Samir Hanash (ORCID: https://orcid.org/0000-0002-4210-1593)
- Kim‐Anh Do (ORCID: https://orcid.org/0000-0001-8710-7131)
- James P. Long (ORCID: https://orcid.org/0000-0001-5853-5938)
- Johannes Francois Fahrmann (ORCID: https://orcid.org/0000-0001-5088-0198)
- Sara Khorami-Sarvestani (ORCID: https://orcid.org/0000-0002-0534-5345)
Institutions
- The University of Texas MD Anderson Cancer Center (US)
Publication Details
- Journal
- Cancer Epidemiology Biomarkers & Prevention
- Published
- 2025-09-25
- DOI
- https://doi.org/10.1158/1055-9965.epi-25-1032
- Citations
- 1
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 0.81