In this episode, we are joined by Dr. Xing to delve into the innovative use of machine learning in predicting the embryonic aneuploidy risk in female IVF patients. Infertility affects approximately 12% of women of reproductive age in the United States, with aneuploidy in eggs significantly contributing to early miscarriages and IVF failures.

Dr. Xing discusses his team's work in using whole-exome sequencing data to evaluate machine learning-based classifiers for predicting aneuploidy risk. Their efforts have achieved encouraging results with the area under the receiver operating curve of 0.77 and 0.68, respectively, across two exome datasets.

This discussion also sheds light on the potential aneuploidy risk genes identified, such as MCM5, FGGY, and DDX60L. These genes and their molecular interaction partners are enriched in meiotic-related gene ontology categories and pathways, like the microtubule organizing center and DNA recombination.

By demonstrating that sequencing data can help predict a patient's aneuploidy risk, Dr. Xing's work opens up new avenues for enhancing clinical diagnosis and provides promising targets for future aneuploidy studies.

Keywords: Dr. Xing, Machine Learning, Embryonic Aneuploidy Risk, Infertility, IVF, Whole-exome Sequencing Data, Receiver Operating Curve, MCM5, FGGY, DDX60L, Meiotic-related Gene Ontology Categories, DNA Recombination, Clinical Diagnosis.

Predicting embryonic aneuploidy rate in IVF patients using whole-exome sequencing https://doi.org/10.1007/s00439-022-02450-z