Department of Mathematics
Mining, modeling, and evaluation of subnetworks from large biomolecular networks and its comparison study
In this paper, we present a novel method to mine, model, and evaluate a regulatory system executing cellular functions that can be represented as a biomolecular network. Our method consists of two steps. First, a novel scale-free network clustering approach is applied to such a biomolecular network to obtain various subnetworks. Second, computational models are generated for the subnetworks and simulated to predict their behavior in the cellular context. We discuss and evaluate some of the advanced computational modeling approaches, in particular, state-space modeling, probabilistic Boolean network modeling, and fuzzy logic modeling. The modeling and simulation results represent hypotheses that are tested against high-throughput biological datasets (microarrays and/or genetic screens) under normal and perturbation conditions. Experimental results on time-series gene expression data for the human cell cycle indicate that our approach is promising for subnetwork mining and simulation from large biomolecular networks. © 2009 IEEE.
Biomolecular network analysis, Fuzzy modeling, Probabilistic Boolean network (PBN) model, State-space model subnetwork mining
Source Publication Title
IEEE Transactions on Information Technology in Biomedicine
Institute of Electrical and Electronics Engineers
Hu, Xiaohua, Michael Ng, Fang-Xiang Wu, and Bahrad A. Sokhansanj. "Mining, modeling, and evaluation of subnetworks from large biomolecular networks and its comparison study." IEEE Transactions on Information Technology in Biomedicine 13.2 (2009): 184-194.