Causal reasoning is a crucial part of science and human intelligence. In order to discover
causal relationships from data, we need structure discovery methods. We provide a review …

To understand complex biological systems requires the integration of experimental and
computational research—in other words a systems biology approach. Computational …

Real world entities work in concert as a system and not in isolation. Understanding the
associations between these entities from their digital signatures can provide novel system …

High-throughput genome-wide molecular assays, which probe cellular networks from
different perspectives, have become central to molecular biology. Probabilistic graphical …

CAM: Causal additive models, high-dimensional order search and penalized regression
Page 1 The Annals of Statistics 2014, Vol. 42, No. 6, 2526–2556 DOI: 10.1214/14-AOS1260 …

Motivation: Network inference algorithms are powerful computational tools for identifying
putative causal interactions among variables from observational data. Bayesian network …

Recent advances in molecular biology such as gene editing [1], bioelectric recording and
manipulation [2] and live cell microscopy using fluorescent reporters [3],[4]–especially with …

Bayesian networks have become a widely used method in the modelling of uncertain
knowledge. Owing to the difficulty domain experts have in specifying them, techniques that …

Motivation: Signaling pathways are dynamic events that take place over a given period of
time. In order to identify these pathways, expression data over time are required. Dynamic …

In this review we give an overview of computational and statistical methods to reconstruct
cellular networks. Although this area of research is vast and fast develo**, we show that …