Mining, modeling, and evaluation of subnetworks from large biomolecular networks and its comparison study

Xiaohua Hu*, Kwok Po Ng, Fang Xiang Wu, Bahrad A. Sokhansanj

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

12 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)184-194
Number of pages11
JournalIEEE Transactions on Information Technology in Biomedicine
Issue number2
Publication statusPublished - Mar 2009

Scopus Subject Areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering

User-Defined Keywords

  • Biomolecular network analysis
  • Fuzzy modeling
  • Probabilistic Boolean network (PBN) model
  • State-space model subnetwork mining


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