Cemal Erdem

This study examines mRNA-protein level correlations in bacterial pathogens under stress, finding reduced correlations and highlighting the role of post-translational modifications in stress adaptation.

This study identifies seven blood biomarkers associated with TGF-β and VHL-HIF signaling in ccRCC patients, showing their potential for prognosis and personalized medicine.

The paper develops an algorithm to simulate and compare mechanistic models of cell viability assays with experimental data to understand drug response variability.

The paper identifies gene regulatory networks linked to GABA signaling in glioblastoma using multi-omics data and machine learning, showing GABA signaling promotes tumor growth through non-synaptic mechanisms.

This paper combines machine learning with mechanistic models to improve cell-line-specific drug response predictions using large datasets.

The paper develops two solutions to communication bottlenecks in large-scale single-cell models, significantly speeding up simulations.

The paper presents a LINCS Microenvironment perturbation dataset cataloging transcriptional, proteomic, epigenomic, and phenotypic responses of MCF10A cells to six ligands, enabling integrative analysis of molecular and phenotypic changes.

MOBILE identifies context-specific molecular networks by integrating multi-omics data, revealing new regulatory mechanisms in immune and developmental pathways.

The paper develops an open-source, scalable mechanistic model for single-cell proliferation and death signaling, demonstrating its application to understand IFNγ's anti-proliferative effects.

The paper develops a computational model of IGF1R and InsR signaling to reveal context-dependent Akt activity in luminal breast cancer cells and identifies RPS6K as a key regulator.