Comprehensive serum glycopeptide spectrum analysis with machine learning for non-invasive early detection of gastrointestinal cancers
CSGSA represents a breakthrough in non-invasive gastrointestinal cancer diagnostics, combining glycopeptide profiling with machine learning to achieve unprecedented accuracy. This method provides a cost-effective and scalable alternative to invasive procedures and may have potential utility in general health screening, which warrants further investigation.
Authors
- Kazuhiro Tanabe (ORCID: https://orcid.org/0000-0003-2671-1217)
- Eiji Kusumoto
- Hideo Uehara
- Hirofumi Hasuda
- Mikio Mikami (ORCID: https://orcid.org/0000-0002-7496-3518)
- Mitsuhiko Ota (ORCID: https://orcid.org/0000-0002-5968-0739)
- Yoshihisa Sakaguchi (ORCID: https://orcid.org/0009-0006-1751-8799)
- Rintaro Yoshida
- Kensuke Kudo
- Chihiro Hayashi (ORCID: https://orcid.org/0009-0001-4411-6369)
- Tetsuya Kusumoto
- Yuichi Hisamatsu (ORCID: https://orcid.org/0000-0001-9828-7369)
Institutions
- Tokai University (JP)
- National Kyushu Medical Center (JP)
- National Hospital Organization Kyushu Cancer Center (JP)
Publication Details
- Journal
- Computational and Structural Biotechnology Journal
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1016/j.csbj.2025.10.067
- Primary Topic
- Glycosylation and Glycoproteins Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00