2 research outputs found
Reverse-Engineering Biological Interaction Networks from Noisy Data using Regularized Least Squares and Instrumental Variables
The problem of reverse engineering the topology
of a biological network from noisy time–series measurements
is one of the most important challenges in the field of Systems
Biology. In this work, we develop a new inference approach
which combines the Regularized Least Squares (RLS) technique
with a technique to avoid the introduction of bias and nonconsistency
due to measurement noise in the estimation of the
parameters in the standard Least Squares (LS) formulation, the
Instrumental Variables (IV) method. We test our approach on a
set of nonlinear in silico networks and show that the combined
exploitation of RLS and IV methods improves the predictions
with respect to other standard approaches