Inference of Gene Regulatory Networks with Sparse Structural Equation Models Exploiting Genetic Perturbations
Figure 1
Block diagram of the sparsity-aware maximum likelihood (SML) algorithm.
The first and third blocks perform cross-validation to select optimal parameters and
to be used in (3) and (4), respectively. The second block produces weights
and error-variance estimate
after solving (4). Finally, the fourth block takes data
and
together with
,
and
and solves (3) to yield
, which represents the SML estimator for
in (1) revealing the genetic-interaction network. A more detailed description of the SML algorithm is given in Algorithm 1 in the Methods section.