Ekin Deniz Aksu

This paper introduces scBoost, a method that enhances cell-type classification by combining single-cell analysis techniques, outperforming existing methods on benchmark datasets.

BatchRefiner improves batch integration of scRNA-seq and scATAC-seq embeddings by ensemble refinement, showing significant performance gains across datasets and metrics.

The paper evaluates single-cell perturbation prediction models by developing a taxonomy and tool to standardize benchmarking protocols across datasets.

The paper introduces Corgi, a context-aware sequence-to-function model that predicts gene expression and epigenetic signals across unseen cell types using DNA sequence and trans-regulator expression.

This paper improves TF binding site prediction using DNA language models and transfer learning, achieving state-of-the-art performance across TFs, cell types, and chromosomes.