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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Summary: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.
ISBN:9780780383760
0780383761
DOI:10.1109/NAFIPS.2004.1337441