This study presents a biogeographical ancestry inference pipeline integrating Principal Component Analysis (PCA) and XGBoost to classify and predict ancestry with high accuracy. The pipeline is optimized for Asian populations, addressing genetic diversity and population structure. By leveraging PCA for dimensionality reduction and XGBoost for robust classification, the model enhances ancestry prediction performance. The application of this method provides valuable insights into genetic ancestry, aiding research in population genetics, personalized medicine, and forensic studies.

Nomination Link: https://forensicscientist.org/award-nomination/?ecategory=Awards&rcategory=Awardee

Website: https://forensicscientist.org/

Social media:

You tube: https://www.youtube.com/@forensicscientistawards

Twitter: https://x.com/AwardForensic

Instagram: https://www.instagram.com/forensicaward/

Pinterest: https://in.pinterest.com/forensicaward/