Ayla Ergün

The paper introduces a high-throughput platform to generate large datasets for training machine learning models to predict antibody developability properties.

The paper develops DRUG-seq to profile drug responses in primary human cells, revealing shared and cell-type-specific transcriptional changes across multiple compounds and concentrations.

The paper evaluates methods for estimating cell-type proportions in brain tissue using paired transcriptomic and immunohistochemistry data, showing some algorithms provide accurate estimates.

Coexpression meta-analysis of 2114 human brain RNA-seq samples across the AMP-AD consortium identifies five consensus Alzheimer's disease transcriptional clusters, including two sex-heterogeneous clusters and a previously unreported proteostasis cluster.