Esra Isik

Genomic analysis is essential for various biological and medical applications, including identifying genetic factors underlying diseases, predicting drug responses, and understanding evolutionary processes. Advances in genomic sequencing technologies have led to an explosion of biological data. However, analyzing genomic data is complex due to its high dimensionality, heterogeneity, and noise. Therefore, it requires sophisticated computational techniques that can handle its complexity and scale.

This project aims to address these challenges by integrating machine learning and statistical methods for advanced genomic analysis. The objectives include developing predictive models for genomic analysis, identifying genetic markers associated with complex traits, investigating mechanisms underlying genetic variation, and improving computational algorithms for analyzing genetic mutations. Also, this project will explore the use of single-cell sequencing technologies to study cellular heterogeneity and dynamics in complex tissues, and develop computational methods for analyzing single-cell genomic data.