Kemal İnecik

Human Developmental Cell Atlas integrates ~4.6 million cells/nuclei from 4-22 post-conception weeks into 454 refined cell types and maps 114 spatial niches across 6PCW whole-embryo sections.

TarDis disentangles multiple covariates in single-cell genomics, improving data integration and out-of-distribution predictions by generating structured latent representations.

IDTrack harmonizes gene identifiers across time and namespaces to ensure reproducibility in computational biology workflows by tracking annotation release history and mapping decisions.

This paper introduces scTRAM, a framework to quantitatively evaluate single-cell trajectory representations, revealing model-specific trade-offs and structural biases in embeddings.

flowVI is a deep generative model for integrating flow cytometry datasets, improving imputation and enabling atlas construction by modeling noise and batch effects.

scARE is a deep learning model that enhances sensitivity to specific gene features while reducing confounding variability, improving downstream performance in single-cell data analysis.

MultiCPA predicts multimodal perturbation responses at the single-cell level using a generative model trained on CRISPR perturbation data.