Title :
Using fuzzy logic inference algorithm to recover molecular genetic regulatory networks
Author :
Yu, JiOg ; Wang, Paul P.
Author_Institution :
Dept. of Electr. & Comput. Eng., Duke Univ., Durham, NC, USA
Abstract :
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.
Keywords :
biology computing; fuzzy logic; genetics; inference mechanisms; causal interactions; computational tools; fuzzy logic inference algorithm; molecular genetic regulatory network recovery; network inference algorithms; Biological system modeling; Computational modeling; Computer networks; Fuzzy logic; Gene expression; Genetics; Inference algorithms; Mathematical model; Power engineering computing; Regulators;
Conference_Titel :
Fuzzy Information, 2004. Processing NAFIPS '04. IEEE Annual Meeting of the
Print_ISBN :
0-7803-8376-1
DOI :
10.1109/NAFIPS.2004.1337441