Multi-marker discovery for mild cognitive impairment in metabolomics using machine learning with a global surrogate model via partial least squares
The method used in this study may be broadly applicable for analyzing multi-marker panels of metabolites and their biological interpretation. This study included an independent validation, and further larger-scale studies using additional external cohorts are warranted to confirm the generalizability of this approach.
Authors
- Tatsuya Mikami (ORCID: https://orcid.org/0000-0002-6657-6446)
- Shuya Kasai (ORCID: https://orcid.org/0000-0001-7171-4885)
- Hiroyuki Yamamoto (ORCID: https://orcid.org/0000-0002-1344-6525)
- Koichi Murashita
- Rira Matsuta
- Ken Itoh (ORCID: https://orcid.org/0000-0002-5518-0729)
- Yoshinori Tamada (ORCID: https://orcid.org/0000-0003-3483-9712)
- Yota Tatara (ORCID: https://orcid.org/0000-0001-8114-3289)
- Kozue Terai
Institutions
- Tsurumi University (JP)
- Hirosaki University (JP)
Publication Details
- Journal
- Metabolomics
- Published
- 2025-11-15
- DOI
- https://doi.org/10.1007/s11306-025-02372-7
- Primary Topic
- Metabolomics and Mass Spectrometry Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00