Burak Yelmen

The paper develops a multi-task neural network to predict blood metabolite profiles by separating covariate, genetic, and joint effects, finding nonlinear covariate modeling improves prediction.

This paper shows that ignoring gene-gene interactions in GWAS can lead to false positive results, highlighting the need for more complex models.

This paper develops a neural network model, nnCNV, to detect CYP2C19 deletions from genotyping array signals, achieving higher accuracy than existing methods.

This study introduces a frugal diffusion model for generating artificial genomes and evaluates its utility for local ancestry inference using synthetic and real data.

The paper generates synthetic genomes using PCA and GANs, showing improved population structure and privacy metrics compared to existing methods.

This paper introduces convolutional and conditional neural networks for generating large-scale artificial genomes with high SNP numbers while minimizing privacy leakage.

This paper uses a deep learning approach to analyze whole-genome sequences and supports the back to Africa model over other hypotheses about human migration.

This study uses generative models to create artificial human genomes that can improve genetic research without compromising privacy.