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Using fuzzy logic inference algorithm to recover molecular genetic regulatory networks

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2 Author(s)
Jing Yu ; Dept. of Electr. & Comput. Eng., Duke Univ., Durham, NC, USA ; Wang, P.P.

Network inference algorithms are powerful computational tools for identifying potential causal interactions among variables from observational data. Fuzzy logic has inherent capability of handling noisy data, so it becomes a tool we use to develop our inference algorithm. Here, we use a simulation approach to test and improve the algorithm. Our fuzzy logic inference algorithm works reasonably well in recovering the underlying regulatory network.

Published in:

Fuzzy Information, 2004. Processing NAFIPS '04. IEEE Annual Meeting of the  (Volume:2 )

Date of Conference:

27-30 June 2004

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