Cellular communication and information processing is performed by complex, dynamic, and
context specific signaling networks. Mathematical modeling is a very useful tool to make …

Systematic study of cell signaling networks increasingly involves high throughput
proteomics, transcriptional profiling, and automated literature mining with the aim of …

The analysis of complex biological networks has traditionally relied on decomposition into
smaller, semi-autonomous units such as individual signaling pathways. With the increased …

Rheumatoid arthritis (RA) is a chronic inflammatory disorder that causes joint pain, swelling,
and loss of function. Development of effective new drugs has proven challenging in part …

Insights into complex, high-dimensional data can be obtained by discovering features of the
data that match or do not match a model of interest. To formalize this task, we introduce the" …

Causal graph discovery is a significant problem with applications across various disciplines.
However, with observational data alone, the underlying causal graph can only be recovered …

We study the connections between causal relations and conditional independence within
the settable systems extension of the Pearl causal model (PCM). Our analysis clearly …

Our goal is to identify beneficial interventions from observational data. We consider
interventions that are narrowly focused (impacting few covariates) and may be tailored to …

Current methods of detecting causal relationships from data rely on analysing the patterns of
correlation among the variables. Given some basic assumptions about how causal …

This work looks at fitting probabilistic graphical models to data when the structure is not
known. The main tool to do this is L₁-regularization and the more general group L₁ …