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-re ...