Deep learning-based genetic disease prediction with Monte Carlo (MC) dropout uncertainty estimation: a comparative study with classical machine learning baselines
- DOI
- 10.3389/fgene.2026.1969295
- Published
- 2026-10-19
- Container
- Frontiers in Genetics
- Publisher
- Frontiers Media SA
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3389/fgene.2026.1969295,
title = {Deep learning-based genetic disease prediction with Monte Carlo (MC) dropout uncertainty estimation: a comparative study with classical machine learning baselines},
author = {V. S. Keerthana and Tarak Hussain},
year = {2026},
journal = {Frontiers in Genetics},
doi = {10.3389/fgene.2026.1969295},
url = {https://doi.org/10.3389/fgene.2026.1969295}
}RIS
TY - JOUR TI - Deep learning-based genetic disease prediction with Monte Carlo (MC) dropout uncertainty estimation: a comparative study with classical machine learning baselines AU - V. S. Keerthana AU - Tarak Hussain PY - 2026 JO - Frontiers in Genetics DO - 10.3389/fgene.2026.1969295 UR - https://doi.org/10.3389/fgene.2026.1969295 ER -
APA
Keerthana, V. S., & Hussain, T. (2026). Deep learning-based genetic disease prediction with Monte Carlo (MC) dropout uncertainty estimation: a comparative study with classical machine learning baselines. Frontiers in Genetics. https://doi.org/10.3389/fgene.2026.1969295
Source records
- crossref · retrieved 2026-11-04T21:09:46.542Z