Background: The biological world is replete with phenomena that appear to be
ideally modeled and analyzed by one archetypal statistical framework - the
Graphical Probabilistic Model (GPM). The structure of GPMs is a uniquely good
match for biological problems that range from aligning sequences to modeling
the genome-to-phenome relationship. The fundamental questions that GPMs address
involve making decisions based on a complex web of interacting factors.
Unfortunately, while GPMs ideally fit many questions in biology, they are not
an easy solution to apply. Building a GPM is not a simple task for an end user.
Moreover, applying GPMs is also impeded by the insidious fact that the complex
web of interacting factors inherent to a problem might be easy to define and
also intractable to compute upon. Discussion: We propose that the visualization
sciences can contribute to many domains of the bio-sciences, by developing
tools to address archetypal representation and user interaction issues in GPMs,
and in particular a variety of GPM called a Conditional Random Field(CRF). CRFs
bring additional power, and additional complexity, because the CRF dependency
network can be conditioned on the query data. Conclusions: In this manuscript
we examine the shared features of several biological problems that are amenable
to modeling with CRFs, highlight the challenges that existing visualization and
visual analytics paradigms induce for these data, and document an experimental
solution called StickWRLD which, while leaving room for improvement, has been
successfully applied in several biological research projects.Comment: BioVis 2014 conferenc