DocumentCode
2416771
Title
Bioinformatics and Fuzzy Logic
Author
Xu, Dong ; Bondugula, Rajkumar ; Popescu, Mihail ; Keller, James
Author_Institution
Missouri Univ., Columbia
fYear
0
fDate
0-0 0
Firstpage
817
Lastpage
824
Abstract
Many biological systems and objects are intrinsically fuzzy. Fuzzy set theory and fuzzy logic are ideal frameworks for describing some biological systems/objects and providing suitable computational methods for a widely range of bioinformatics problems. In this paper, we present two examples of using fuzzy set theory in bioinformatics, one in fuzzy measurement of ontological similarity and its application in bioinformatics, and the other in the application of the fuzzy k-nearest neighbor algorithm in protein secondary structure prediction. We also review other "fuzzy" methods for bioinformatics applications.
Keywords
biology computing; fuzzy logic; fuzzy set theory; molecular biophysics; ontologies (artificial intelligence); proteins; bioinformatics; biological system; fuzzy k-nearest neighbor algorithm; fuzzy logic; fuzzy measurement; fuzzy set theory; ontological similarity; protein secondary structure prediction; Bioinformatics; Biological systems; Biology computing; Data analysis; Fuzzy logic; Fuzzy set theory; Gene expression; Genomics; Ontologies; Proteins;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems, 2006 IEEE International Conference on
Conference_Location
Vancouver, BC
Print_ISBN
0-7803-9488-7
Type
conf
DOI
10.1109/FUZZY.2006.1681805
Filename
1681805
Link To Document