Erdal Coşgun

Prediction of Polygenic Risk Score by Machine Learning and Deep Learning Methods in Genome-wide Association Studies

The paper compares machine learning and deep learning methods to classical approaches for calculating polygenic risk scores using GWAS data, finding ML methods provide more consistent case-control separation.

Boosting GPT Models for Genomics Analysis: Generating Trusted Genetic Variant Annotations and Interpretations through RAG and fine-tuning

This paper enhances GPT models for genomics by integrating variant annotations via RAG and fine-tuning, improving variant interpretation accuracy and efficiency.

Known pathogenic gene variants and new candidates detected in Sudden Unexpected Infant Death using Whole Genome Sequencing

This study identifies known pathogenic variants and new candidates in SUID using WGS, highlighting genetic factors related to heart disease and redox metabolism.