Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Constraint Minimization for Efficient Modeling of Gene Regulatory Network
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Generating Synthetic Gene Regulatory Networks
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
A Markov-Blanket-Based Model for Gene Regulatory Network Inference
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
| Hi-index | 0.00 |
The inference of a network structure from microarray data providing dynamical information about the underlying Gene regulatory network is an important and still outstanding problem. Recently, a causal modeling approach was presented in our publications to recover the structure of this network. However, issues like spurious arcs and time delay were not dealt with previously. The graph-theoretical measure d-separation provides criteria to recover the network structure edge-by-edge by calculating the partial correlation. However, the estimation of partial correlations from small sample sizes is a practical problem. As our approach attempts to find networks that closely match the observed partial correlation constraints in the data, main aim to this paper is to attempt to maximize the scoring metric used. In this paper, we formulate a framework for path analysis as a post processing step after learning gene regulatory network using causal modeling. The approach is tested with both artificial and real gene regulatory network scenario and the structure recovered after post processing better fits the data.