An Evolutionary Algorithm Taking Account of Mutual Interactions among Substances for Inference of Genetic Networks   [EA] [GN]

by

Ono, I., Seike, Y., Morishita, R., Ono, N. and Matsui, M.

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Info: Proceedings of the 2004 IEEE Congress on Evolutionary Computation (Conference proceedings), 2004, p. 2060-2067
Keywords:Genome Informatics
Abstract:
In this paper, we improve NSS-EA that is a search method for inference of genetic networks [GN] by S-system. NSS-EA is an excellent method form the viewpoints of "efficient search of a set of satisfactory structures" and "search of structures satisfying biological knowledge". However, it has a problem from the viewpoint of "search of the true structure". To solve the problem, first, we improve the parameter search process [PS] by using multiple time course data when evaluating genetic networks. Second, [GN] we propose four new structure-search operators taking account of mutual interactions among substances. We show the effectiveness of the proposed improvements by applying it a five-substance benchmark problem.
Notes:
CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.
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BibTex:
@InProceedings{Ono:2004:AEATAoMIaSfIoGN,
  title     = {An Evolutionary Algorithm Taking Account of Mutual Interactions among Substances for Inference of Genetic Networks},
  author    = {Isao Ono and Yoshiaki Seike and Ryohei Morishita and Norihiko Ono and Masahiko Matsui},
  pages     = {2060--2067},
  booktitle = {Proceedings of the 2004 IEEE Congress on Evolutionary Computation},
  year      = {2004},
  publisher = {IEEE Press},
  month     = {20-23 June},
  address   = {Portland, Oregon},
  ISBN      = {0-7803-8515-2},
  keywords  = {Genome Informatics},
  abstract  = {
In this paper, we improve NSS-EA that is a search method for inference of
genetic networks by S-system. NSS-EA is an excellent method form the
viewpoints of "efficient search of a set of satisfactory structures" and
"search of structures satisfying biological knowledge". However, it has a
problem from the viewpoint of "search of the true structure". To solve the
problem, first, we improve the parameter search process by using multiple time
course data when evaluating genetic networks. Second, we propose four new
structure-search operators taking account of mutual interactions among
substances. We show the effectiveness of the proposed improvements by applying
it a five-substance benchmark problem.
},
  notes     = {CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.},
}