Leveraging mixed-effects regression trees for the analysis of high-dimensional longitudinal data to identify the low and high-risk subgroups: simulation study with application to genetic study
The results showed that the unbiased RE-EM and Ev-RE-EM algorithms outperformed the RE-EM algorithm. Since algorithm performance varies across datasets, researchers should test different algorithms on the dataset of interest and select the best-performing one. Accurately predicting and diagnosing an individual's genetic profile is crucial in medical studies. The model with the highest accuracy should be used to enhance understanding of the genetics of complex traits, improve disease prevention and diagnosis, and aid in treating complex human diseases.
Authors
- Mahdi Akbarzadeh (ORCID: https://orcid.org/0000-0002-8048-744X)
- Anoshirvan Kazemnejad (ORCID: https://orcid.org/0000-0002-0143-9635)
- Mina Jahangiri (ORCID: https://orcid.org/0000-0001-9119-7571)
- Keith Goldfeld (ORCID: https://orcid.org/0000-0002-0292-8780)
- Maryam S. Daneshpour (ORCID: https://orcid.org/0000-0003-1525-8672)
- Shayan Mostafaei (ORCID: https://orcid.org/0000-0002-1966-1306)
- Mehdi Momen (ORCID: https://orcid.org/0000-0002-2562-2741)
- Davood Khalili (ORCID: https://orcid.org/0000-0003-4956-1039)
Institutions
- University of Wisconsin–Madison (US)
- Tarbiat Modares University (IR)
- Karolinska Institutet (SE)
- Research Institute for Endocrine Sciences (IR)
- New York University (US)
- Shahid Beheshti University of Medical Sciences (IR)
Publication Details
- Journal
- BioData Mining
- Published
- 2025-03-19
- DOI
- https://doi.org/10.1186/s13040-025-00437-w
- Citations
- 2
- Primary Topic
- Genetic and phenotypic traits in livestock
- Type
- article
- Field-Weighted Citation Impact
- 3.63