Using fuzzy logic inference algorithm to recover molecular genetic regulatory networks

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 a...

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Bibliographic Details
Published in:	NAFIPS - 2004 Annual Meeting of the North American Fuzzy Information Processing Society Vol. 2; pp. 990 - 995 Vol.2
Main Authors:	Jing Yu, Wang, P.P.
Format:	Conference Proceeding
Language:	English
Published:	Piscataway NJ IEEE 2004
Subjects:	Applied sciences Artificial intelligence Biological system modeling Computational modeling Computer networks Computer science; control theory; systems Exact sciences and technology Fuzzy logic Gene expression Genetics Inference algorithms Mathematical model Power engineering computing Regulators Fuzzy logic Fuzzy algorithm Data handling Genetic algorithm Inference Causality Gene expression Regulatory graph Signal to noise ratio
ISBN:	9780780383760, 0780383761
Online Access:	Get full text
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