Integrating omics data and machine learning techniques for precision detection of oral squamous cell carcinoma: evaluating single biomarkers
Our findings demonstrate the utility of integrating omics data with advanced machine learning techniques to develop accurate, non-invasive diagnostic tools for OSCC. The study highlights actionable metabolic signatures that have potential applications in personalized therapeutics and early intervention strategies.
Authors
- Guozhen Cheng
- Yilan Sun (ORCID: https://orcid.org/0009-0008-2996-1733)
- Jiacheng Luo (ORCID: https://orcid.org/0000-0002-8582-0707)
- Dongliang Wei
- Jiannan Liu
Institutions
- Shanghai Jiao Tong University (CN)
- National Clinical Research Center for Digestive Diseases (CN)
- Shanghai Stomatological Hospital (CN)
- Shanghai Ninth People's Hospital (CN)
- Fujian Agriculture and Forestry University (CN)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2024-12-03
- DOI
- https://doi.org/10.3389/fimmu.2024.1493377
- Citations
- 6
- Primary Topic
- Gene expression and cancer classification
- Type
- article
- Field-Weighted Citation Impact
- 1.40
Funders
- National Natural Science Foundation of China
- Shanghai Jiao Tong University