Peer-reviewedCC BY-NC-ND 4.0Scientific Reports
Multimodal ensemble machine learning pipeline for oocyte fertilization prediction using first polar body morphology and clinical factors
Related
- Machine learning-based prediction of clinical pregnancy in infertile women undergoing in vitro fertilization
- A hybrid deep learning and ensemble approach for accurate prediction of hepatocellular carcinoma from sequence variants of hepatitis B virus (HBV) X gene
- An explainable ensemble machine learning approach for coefficient of compressibility prediction of Chengdu clay
- Sustainable hemocompatibility prediction of electrospun nanomaterials using SEM-driven CNN ensemble learning aligned with ISO 10993
- Stacking ensemble learning for PCC voltage prediction in SEIG-ELC based off-grid micro-hydro systems
BibTeX
@article{onthuam2026multimodal,
title = {{Multimodal ensemble machine learning pipeline for oocyte fertilization prediction using first polar body morphology and clinical factors}},
author = {Krittapat Onthuam and Tinapat Limsila and Thanakorn Sappakit and Methawin Vongsaisuwon and Ronnapee Chaichaowarat and Chanakarn Suebthawinkul},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-77149-6},
url = {https://www.nature.com/articles/s41598-026-77149-6},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Multimodal ensemble machine learning pipeline for oocyte fertilization prediction using first polar body morphology and clinical factors
AU - Krittapat Onthuam
AU - Tinapat Limsila
AU - Thanakorn Sappakit
AU - Methawin Vongsaisuwon
AU - Ronnapee Chaichaowarat
AU - Chanakarn Suebthawinkul
PY - 2026
DA - 2026/10/27
JO - Scientific Reports
DO - 10.1038/s41598-026-77149-6
UR - https://www.nature.com/articles/s41598-026-77149-6
SN - 2045-2322
ER -