Theo Knijnenburg

The paper develops a Functional Module States framework to predict drug sensitivity by analyzing transcriptional states from gene expression data in breast cancer cell lines.

The paper introduces Foresight, a method to interpret Random Forest models in biological datasets by uncovering feature groups and interactions, demonstrating its application on breast cancer gene expression data.

The paper constructs intercellular signaling networks in cancer tissues using ligand-receptor expression patterns from whole-tumor and single-cell transcriptomes to identify cell communication structures influencing cancer behaviors.

The paper introduces LOBICO, a logic modeling approach for binary inputs predicting continuous outputs, showing it outperforms single-gene predictors in cancer drug response.

The paper introduces an empirical adaptation of Brown’s method to combine dependent P-values, showing it outperforms Fisher’s method on simulated and TCGA data.