Idil Tuncali

The paper fine-maps PD GWAS signals across cell types, revealing convergent glial-neuronal risk genes and a shared vesiculopathy network.

This paper develops computational tools for allelic imbalance analysis using single-nucleus RNA-seq and demonstrates their application in a Parkinson's disease cohort.

This study integrates long-read WGS and single-nucleus RNA-seq to identify structural variants affecting gene expression in Parkinson's disease with cell type resolution.

The paper uses proteomics and machine learning to identify potential biomarkers in cerebrospinal fluid for Parkinson's disease, showing altered protein levels and neuroinflammation signatures.

The paper proposes DeepMNE-CNN, a method that integrates multiple biological networks using semi-supervised autoencoders and convolutional neural networks to improve gene function prediction.