Description:
Inferring groups of interacting
proteins or genes with biological
significance is a main trend of the current bioinformatics
research,
as this task can help in revealing the functionality and the
relevance of specific macromolecular assemblies or even in
discovering possible macromolecules affecting a specific
biological
process. Protein and gene interaction networks can be modeled
similarly to social interaction networks, so that these
biologically
significant groups correspond to communities. Reliable
algorithms
able to discover such communities may increase knowledge about
biological functions at a molecular level, and may support drug
discovery and enhance disease treatments even in earlier stages.
This
project is aimed at the development of effective tools for
community
detection in biological networks using methods of network and
graph
theories, machine learning, and computational intelligence. For
instance, a significant application goal, important for cancer
biomarker research, is a better understanding of the role of
miRNAs,
a novel class of non-coding RNA able to modulate the expression
of
their “target” genes. The available algorithms, mostly based on
structural information, are still not able to provide a
biological
enrichment of their results, that can instead be obtained from
the
proposed analysis.
Requirements:
background in computer science, bioengineering, computer
engineering,
physics or related disciplines.
Contacts:
stefano.rovetta@unige.it