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Please use this identifier to cite or link to this item: http://hdl.handle.net/1860/2699

Title: A novel approach for mining and fuzzy simulation of subnetworks from large biomolecular networks
Authors: Hu, Xiaohua
Sokhansanj, Bahrad
Wu, Daniel
Tang, Yuchun
Keywords: Biomedical Literature Mining;Biomolecular Network;Fuzzy Logic;Information Extraction;Subnetwork
Issue Date: Dec-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: IEEE Transactions on Fuzzy Systems, 15(6): pp.1219-1229.
Abstract: Understanding the biomolecular network implementing cellular function goes beyond the old dogma of “one gene: one function”; only through comprehensive system understanding can we predict the impact of genetic variation in the population, design effective disease therapeutics, and evaluate the potential side-effects of therapies. In this paper, we present a novel method to model the regulatory system that executes a cellular function, which can be represented as a biomolecular network. Our method consists of three steps. First, the biomolecular network is derived using data-mining approaches to extend the initial conceptual biomolecular network from the literature search, etc. Secondly, once the whole biomolecular network structure is complete, a novel scale-free network clustering approach is applied to obtain various subnetworks. Lastly, fuzzy rule based models are generated for the subnetworks and simulations are run to predict their behavior in the cellular context. The modeling results represent hypotheses that are tested against high-throughput data sets (microarrays and/or genetic screens) for both the natural system and perturbations. If computational results do not match experimental or previously published results, then new hypotheses are formed and they feed back into the data-mining and analyzing step to refine the biomolecuar network for the next iteration. This is repeated until a good match between modeling and data is obtained. Notably, the dynamic modeling component of this method depends on the automated network structure generation of the first component and the subnetwork clustering, which are both essential to make the solution tractable. Experimental results on human gene interaction networks and gene expression time series data for the human cell cycle indicate that our approach is promising for subnetwork mining and simulation from large biomolecular networks, as it produces a better convergence between continuous modeling and experiments.
URI: http://dx.doi.org/10.1109/TFUZZ.2007.896248
Appears in Collections:Faculty Research and Publications (IST)

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