Safiye Çelik

This paper builds and benchmarks perturbative maps of transcriptional and morphological data, demonstrating their utility for discovering new biology and highlighting the importance of standardized frameworks.

The paper identifies a proximity bias in CRISPR-Cas9 knockouts, showing phenotypic similarity between unrelated genes on the same chromosome arm, and proposes a correction method.

The paper introduces DeepProfile, a deep learning framework for analyzing gene expression in 18 cancers, revealing biological insights through latent space analysis and linking gene expression to clinical outcomes.

RxRx3 provides a large-scale high-content screening dataset combining CRISPR knockouts and small molecule treatments to enable machine learning research and biological discovery.

This paper introduces a novel unsupervised pathway attribution method that identifies major transcriptomic variation sources when combined with biologically-constrained models, applied to Alzheimer's disease brain samples.

The paper introduces MD-AD, a deep learning framework that identifies non-linear gene expression-neuropathology relationships in Alzheimer's disease using multi-cohort data, revealing sex-specific immune response patterns.

DECODER integrates large-scale gene expression data to identify mitochondrial Complex I as a therapeutic target for Alzheimer’s disease.

MD-AD improves prediction of Alzheimer's disease hallmarks and staging systems by combining heterogeneous datasets with multi-task deep learning.