Åsmund Flobak

This study investigates synergistic effects of kinase inhibitor combinations by analyzing phosphoproteomics and transcriptomics data to identify molecular mechanisms and pathway interactions.

TRAFIKK predicts and explains anticancer drug synergies by simulating drug effects in cell-specific models, achieving high recall and revealing both conserved and context-dependent mechanisms.

NeKo automates biological network construction by integrating molecular interactions from databases, enabling efficient network building for research applications.

This study uses metabolic modeling and transcriptomics to analyze drug-induced metabolic changes in gastric cancer cells, revealing synergistic effects and potential therapeutic targets.

BooLEVARD quantifies paths leading to node activation or repression in Boolean models, enhancing signal strength analysis in signaling networks.

This paper introduces a method to apply Cell Painting to 3D spheroids for morphological profiling, detecting phenotypic changes in colorectal cancer cells under compound treatments.

The paper develops a standardized data model for cancer genomics to enable interoperable data sharing across countries.

The paper develops a dynamic Boolean model to simulate psoriasis development and identifies potential drug targets by predicting interventions that mimic current treatments.

The paper predicts drug synergies using a logical modeling framework that combines manual curation with automated parameterization, showing that automated methods can outperform manual models in some cases.

The paper presents a large-scale cancer pharmacogenomic screen with 11,576 experiments on 910 drug combinations across 85 cancer cell lines, enabling crowd-sourced prediction of synergistic drug pairs and biomarkers.

This paper generalizes the Bliss independence model to allow arbitrary maximal effects for dose-response curves in drug combinations while maintaining statistical independence assumptions.