Comparative analysis of machine learning techniques in metabolomic-based preterm birth prediction
Our results highlight the complexity of metabolomics-based modelling for preterm birth and support an iterative, model-driven approach for optimizing predictive accuracy in small-scale clinical datasets.
Authors
- Suzanne Tough (ORCID: https://orcid.org/0000-0001-5583-7404)
- Jane Shearer (ORCID: https://orcid.org/0000-0002-1224-979X)
- Chunlong Mu (ORCID: https://orcid.org/0000-0001-9405-5907)
- Gavin E. Duggan (ORCID: https://orcid.org/0000-0002-9869-4447)
- Donna M. Slater (ORCID: https://orcid.org/0000-0003-2811-3632)
- Ying-Chieh Han
Institutions
- Google (United States) (US)
- University of Calgary (CA)
- Alberta Children's Hospital (CA)
Publication Details
- Journal
- Computational and Structural Biotechnology Journal
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1016/j.csbj.2025.07.010
- Primary Topic
- Metabolomics and Mass Spectrometry Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Children's Hospital Foundation
- Alberta Children's Hospital Foundation
- Alberta Innovates
- Natural Sciences and Engineering Research Council of Canada