Inference of Gene Regulatory Networks using S-System: A Unified Approach
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Wang:2007:CIBCB,
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author = "Haixin Wang and Lijun Qian and Edward Dougherty",
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title = "Inference of Gene Regulatory Networks using S-System:
A Unified Approach",
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booktitle = "IEEE Symposium on Computational Intelligence and
Bioinformatics and Computational Biology, CIBCB '07",
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year = "2007",
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editor = "Gwenn Volkert",
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pages = "82--89",
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address = "Honolulu",
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month = "1-5 " # apr,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "1-4244-0710-9",
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URL = "http://old.pvamu.edu/edir/lijun/files/papers/CIBCB2007.pdf",
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size = "8 pages",
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abstract = "In this paper, a unified approach to infer gene
regulatory networks using the S-system model is
proposed. In order to discover the structure of
large-scale gene regulatory networks, a simplified
S-system model is proposed that enables fast parameter
estimation to determine the major gene interactions. If
a detailed S-system model is desirable for a subset of
genes, a two-step method is proposed where the range of
the parameters will be determined first using genetic
programming and recursive least square estimation. Then
the exact values of the parameters will be calculated
using a multi-dimensional optimisation algorithm. Both
downhill simplex algorithm and modified Powell
algorithm are tested for multi-dimensional
optimization. Simulation results using both synthetic
data and real microarray measurements demonstrate the
effectiveness of the proposed methods",
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notes = "http://www.cs.kent.edu/~volkert/CIBCB07/sessions.html
2 and 5 genes, microarray, time series gene expression,
z-score fitness
INSPEC Accession Number: 9507409
",
- }
Genetic Programming entries for
Haixin Wang
Lijun Qian
Edward R Dougherty
Citations