  One of the most interesting, difficult, and potentially useful topics in
computational biology is the inference of gene regulatory networks (GRNs) from
expression data. Although researchers have been working on this topic for more
than a decade and much progress has been made, it remains an unsolved problem
and even the most sophisticated inference algorithms are far from perfect. In
this paper, we review the latest developments in network inference, including
state-of-the-art algorithms like PIDC, Phixer, and more. We also discuss
unsolved computational challenges, including the optimal combination of
algorithms, integration of multiple data sources, and pseudo-temporal ordering
of static expression data. Lastly, we discuss some exciting applications of
network inference in cancer research, and provide a list of useful software
tools for researchers hoping to conduct their own network inference analyses.
