Abstract. We present an approach to inferring probabilistic models of generegulatory networks that is intended to provide a more mechanistic representation of transcriptional regul...
Learning temporal graph structures from time series data reveals important dependency relationships between current observations and histories. Most previous work focuses on learn...
Abstract: It is often challenging to reconstruct accurately a complete dynamic biological network due to the scarcity of data collected in cost-effective experiments. This paper ad...
Relational graphs are widely used in modeling large scale networks such as biological networks and social networks. In this kind of graph, connectivity becomes critical in identif...
Abstract. In systems biology, the number of models of cellular processes increases rapidly, but re-using models in different contexts or for different questions remains a challengi...