Walter Kölch

The paper identifies SMAD3-driven cell state transitions as a mechanism of immunoresistance in bladder cancer and shows that SMAD3 inhibition sensitizes tumors to T cell-mediated cytotoxicity.

The paper identifies T-cell-Macrophage interactions in ovarian cancer that may influence chemotherapeutic response by modulating T-cell exhaustion and polarization.

The paper identifies JNK-independent drugs that, when combined with standard chemotherapy, improve response in relapsed high-risk neuroblastoma by exploiting differential apoptotic network utilization.

This study identifies a synergistic combination of DPI and MitoQ that targets mitochondrial metabolism in MYCN-amplified neuroblastoma cells, enhancing chemotherapeutic efficacy.

This study develops a method to infer physical cell-cell communication networks from scRNAseq data by identifying multiplets and using univariate linear models to predict interactions.

This study uses proteomics and immunohistochemistry to analyze tumor-immune dynamics in HGSOC, identifying mechanisms of immune escape across metastatic progression.

The paper identifies a selective JNK inhibitor that blocks metastasis in triple-negative breast cancer by targeting metabolic pathways.

SAEG is a deep learning model that predicts gene knockout susceptibility by integrating mutations, copy number alterations, and protein interactions, outperforming existing methods.

This study identifies diphenyleneiodonium chloride as a compound that targets MYCN-amplified neuroblastoma by inducing proteasomal degradation of MYCN and reversing mitochondrial alterations.

The paper identifies transcriptional changes from physical cell-cell interactions that predict ovarian cancer patient responses to therapy.

This study uses spatial transcriptomics and proteomics to identify dysregulated genes and proteins in placenta increta, revealing ECM degradation, immune suppression, and epithelial-mesenchymal transition promoting trophoblast invasion.

This study profiles transcriptional changes during early chondrogenic differentiation of human MSCs, identifying key regulatory processes starting within 2 hours of induction.

KBoost infers gene regulatory networks from gene expression data using kernel PCA regression, boosting, and Bayesian model averaging, showing improved performance across datasets and applied to breast cancer data.

The paper introduces a model using knowledge graphs to predict kinase-substrate networks, achieving higher coverage and accuracy than existing methods through network-based learning.

The paper shows how periodic GTPase waves coordinate leading and trailing edge dynamics during cell migration through RhoA-Rac1 interactions.

This study identifies transcriptional regulatory mechanisms in mouse lung tissue exposed to carbon nanotubes using gene regulatory network analysis.