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A novel approach for mining and fuzzy simulation of subnetworks from large biomolecular networks
Please use this identifier to cite or link to this item:
http://hdl.handle.net/1860/2699
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| 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
http://hdl.handle.net/1860/2699 |
| Appears in Collections: | Faculty Research and Publications (IST)
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