Fuzzy association rules for biological data analysis: a case study on yeast Export

@article{citeulike:2398819, abstract = {BACKGROUND:Last years' mapping of diverse genomes has generated huge amounts of biological data which are currently dispersed through many databases. Integration of the information available in the various databases is required to unveil possible associations relating already known data. Biological data are often imprecise and noisy. Fuzzy set theory is specially suitable to model imprecise data while association rules are very appropriate to integrate heterogeneous data.RESULTS:In this work we propose a novel fuzzy methodology based on a fuzzy association rule mining method for biological knowledge extraction. We apply this methodology over a yeast genome dataset containing heterogeneous information regarding structural and functional genome features. A number of association rules have been found, many of them agreeing with previous research in the area. In addition, a comparison between crisp and fuzzy results proves the fuzzy associations to be more reliable than crisp ones.CONCLUSIONS:An integrative approach as the one carried out in this work can unveil significant knowledge which is currently hidden and dispersed through the existing biological databases. It is shown that fuzzy association rules can model this knowledge in an intuitive way by using linguistic labels and few easy-understandable parameters.}, author = {Lopez, Francisco and Blanco, Armando and Garcia, Fernando and Cano, Carlos and Marin, Antonio}, citeulike-article-id = {2398819}, citeulike-linkout-0 = {http://dx.doi.org/10.1186/1471-2105-9-107}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/18284669}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=18284669}, doi = {10.1186/1471-2105-9-107}, journal = {BMC Bioinformatics}, keywords = {data\_integration, fuzzy\_logic, statistics}, number = {1}, posted-at = {2008-03-28 16:51:21}, priority = {2}, title = {Fuzzy association rules for biological data analysis: a case study on yeast}, url = {http://dx.doi.org/10.1186/1471-2105-9-107}, volume = {9}, year = {2008} }

TY - JOUR ID - citeulike:2398819 L3 - citeulike-article-id:2398819 N2 - BACKGROUND:Last years' mapping of diverse genomes has generated huge amounts of biological data which are currently dispersed through many databases. Integration of the information available in the various databases is required to unveil possible associations relating already known data. Biological data are often imprecise and noisy. Fuzzy set theory is specially suitable to model imprecise data while association rules are very appropriate to integrate heterogeneous data.RESULTS:In this work we propose a novel fuzzy methodology based on a fuzzy association rule mining method for biological knowledge extraction. We apply this methodology over a yeast genome dataset containing heterogeneous information regarding structural and functional genome features. A number of association rules have been found, many of them agreeing with previous research in the area. In addition, a comparison between crisp and fuzzy results proves the fuzzy associations to be more reliable than crisp ones.CONCLUSIONS:An integrative approach as the one carried out in this work can unveil significant knowledge which is currently hidden and dispersed through the existing biological databases. It is shown that fuzzy association rules can model this knowledge in an intuitive way by using linguistic labels and few easy-understandable parameters. IS - 1 JF - BMC Bioinformatics TI - Fuzzy association rules for biological data analysis: a case study on yeast VL - 9 KW - data_integration KW - fuzzy_logic KW - statistics AU - Lopez, Francisco AU - Blanco, Armando AU - Garcia, Fernando AU - Cano, Carlos AU - Marin, Antonio PY - 2008/// UR - http://dx.doi.org/10.1186/1471-2105-9-107 ER -