Department of Computer, Control and Management Engineering

The article takes as a starting point, the observation of a deep and long-standing gap be­
tween the views of biologists/physicians and that of physicists/data scientists when deal­
ing with life sciences. This gap has been exacerbated by the advent of large-scale -omics
technologies that, have produced large amounts of molecular data impossible to handle
in the conventional way. Consequently, it prevents the advancement, of interdisciplinary
research that, should be able to “make (biological) sense of (biological) data”. Indeed, on
the one hand, “life scientists” tend to define their discipline as independent, of any math­
ematical formalization and, on the other hand, “number scientists” are in search of uni­
versal principles based on mathematical laws, Here, we focus on the impact of this gap
in the field of precision medicine that, impedes dialogue between omics data analysist.s
and precision medicine physicians, because of the profound differences in cultural back­
ground and mindset between the two, To try to overcome (or reduce) this cultural divide,
the article suggests a new possibility through network science and the use of a common
language. Such common language is a shared vocabulary of words that, have different,
meanings in each discipline but refer to the same “thing” (cell behavior, health, disease,
etc,), For example, the concept, of “community” on a network is an important topic of
research and several algorithms are available for finding such communities (or clusters,
modules). By contrast,, from a biological/medical perspective, the same concept, has dif­
ferent. meanings. A community of genes, proteins, or patients is there because they share
some common molecular mechanism or purpose (function), This simple example makes
it. clear that we can move directly from biological concepts to network patterns and
algorithms and backwards, thus generating a true dialogue between “life scientists” and
“number scientists”, even though each remains in its own cultural domain. This latter
point, is very important, because physicians are often intimidated by mathematics and
data scientists are usually interested in the algorithms themselves and not. in their bio­
logical significance. The article presents several simple network concepts and algorithms
relevant to precision medicine as a starting point for a true interdisciplinary dialogue.