This is a short post from the 11-th International Conference on Computational Methods in System Biology.
We presented our paper: “Constraint Programming in Community-based Gene Regulatory Network Inference”. The talk focused on the use of constraints programming to in the context of gene regulatory network inference. Gene Regulatory Network (GRN) inference is a major objective of
Systems Biology. The complexity of biological systems and the lack of
adequate data have posed many challenges to the inference
problem. Community networks integrate predictions from
individual methods in a “meta predictor”, in order to compose the
advantages of different methods and soften individual
limitations. This paper proposes a novel methodology to integrate
prediction ensembles using Constraint Programming, a declarative
modeling paradigm, which allows the formulation of dependencies among
components of the problem, enabling the integration of diverse forms
of knowledge. We show the potential of this
method: the addition of biological constraints can offer improvements
in the prediction accuracy, and the method shows promising results in
assessing biological hypothesis using constraints.
Here you can find the slides presented at CMSB 13.