Multi-Omics Feature Selection to Identify Biomarkers for Hepatocellular Carcinoma
The findings in this study reinforce the importance of adapting or extending deep learning models to support robust feature selection, especially for integration of multi-omics data with limited sample size to avoid the risk of overfitting and the need for evaluation of the multi-omics features discovered in this study via blood samples from a larger and independent cohort to identify robust biomarkers for HCC.
Authors
- Sivagami Giridharan
- Alexander Kroemer (ORCID: https://orcid.org/0000-0002-9842-4973)
- Habtom W. Ressom (ORCID: https://orcid.org/0000-0001-9296-2132)
- Rency S. Varghese (ORCID: https://orcid.org/0000-0002-4236-7316)
- Muhammad Salman Sajid (ORCID: https://orcid.org/0000-0003-0176-7855)
- Xinran Zhang (ORCID: https://orcid.org/0000-0001-9049-2691)
- Md. Mamunur Rashid (ORCID: https://orcid.org/0000-0001-6849-587X)
Institutions
- Georgetown University (US)
- Georgetown University Medical Center (US)
- MedStar Georgetown University Hospital (US)
Publication Details
- Journal
- Metabolites
- Published
- 2025-08-28
- DOI
- https://doi.org/10.3390/metabo15090575
- Primary Topic
- Metabolomics and Mass Spectrometry Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institutes of Health