Péter Csermely

The paper analyzes the C. elegans neuronal network using signed motif analysis to identify overrepresented positive feedforward and negative feedback loops.

This paper introduces probabilistic edge-weight operators to improve Boolean network dynamics by incorporating noise and state-dependent edge weights, enhancing prediction accuracy in regulatory systems.

The paper predicts synaptic polarity and sign-balance in C. elegans using gene expression data and connectivity information, finding an excitatory-to-inhibitory ratio of approximately 4:1.