Chinmaya U. Joisa

The paper identifies combinatorial KRAS/FGFR dependence in pancreatic cancer through genetic interaction mapping and shows that combined inhibition overcomes resistance.

This study shows that daraxonrasib, a pan-RAS(on) inhibitor, effectively inhibits TNBC cell proliferation and tumor growth, either alone or in combination with a MEK inhibitor.

The paper uses proteomic kinome profiling and machine learning to predict kinase inhibitor cardiotoxicity by analyzing off-target kinase engagement and its association with cardiac adverse events.

This study quantifies kinase protein abundance in human tissues using proteomic mass spectrometry, revealing heterogeneity in breast cancer subtypes and enabling clinical applications.

The paper uses machine learning to predict clinical outcomes in pediatric Crohn's disease by analyzing gene expression from ileal and colonic tissues.

The paper links kinase inhibitor treatment effects to cell line responses using integrated kinome profiling data and builds predictive models with high accuracy.

The paper predicts cancer cell line sensitivity to kinase inhibitor combinations using kinome inhibition states and machine learning, achieving high accuracy in both simulated and experimental settings.

The paper predicts cell viability in cancer cell lines using kinase inhibitor profiles and gene expression data, achieving R2 of 0.78 and RMSE of 0.154.

The paper links kinome state with cell viability in PDAC tumor and stroma cell lines using regression and classification models, finding predictive relationships and key kinases driving viability responses.