GENECFE-ANFIS: A NEURO-FUZZY INFERENCE SYSTEM TO INFER GENE-GENE INTERACTIONS BASED ON RECOGNITION OF MICROARRAY GENE EXPRESSION PATTERNS

Abstract

Biomedical Engineering: Applications, Basis and CommunicationsVol. 19, No. 02, pp. 71-78 (2007) No AccessGENECFE-ANFIS: A NEURO-FUZZY INFERENCE SYSTEM TO INFER GENE-GENE INTERACTIONS BASED ON RECOGNITION OF MICROARRAY GENE EXPRESSION PATTERNSCheng-Long Chuang, Chung-Ming Chen, Grace S. Shieh, and Joe-Air JiangCheng-Long ChuangInstitute of Biomedical Engineering, National Taiwan University, Taipei, 106, TaiwanDepartment of Bio-Industrial Mechatronics Engineering, National Taiwan University, Taipei, 106, TaiwanInstitute of Statistical Science, Academia Sinica, Taipei, 115, Taiwan Search for more papers by this author , Chung-Ming ChenInstitute of Biomedical Engineering, National Taiwan University, Taipei, 106, Taiwan Search for more papers by this author , Grace S. ShiehInstitute of Statistical Science, Academia Sinica, Taipei, 115, Taiwan Search for more papers by this author , and Joe-Air JiangDepartment of Bio-Industrial Mechatronics Engineering, National Taiwan University, Taipei, 106, TaiwanCorresponding author: Joe-Air Jiang, Associate Professor, Ph.D., Department of Bio-Industrial Mechatronics Engineering, National Taiwan University, No., 1, Sec. 4, Roosevelt Rd., Taipei, 106, Taiwan. Tel.: +886-2-3366-5341; Fax: +886-2-2362-7620. Search for more papers by this author https://doi.org/10.4015/S1016237207000112Cited by:3 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail AbstractA neuro-fuzzy inference system that recognizes the expression patterns of genes in microarray gene expression (MGE) data, called GeneCFE-ANFIS, is proposed to infer gene interactions. In this study, three primary features are utilized to extract genes' expression patterns and used as inputs to the neuro-fuzzy inference system. The proposed algorithm learns expression patterns from the known genetic interactions, such as the interactions confirmed by qRT-PCR experiments or collected through text-mining technique by surveying previously published literatures, and then predicts other gene interactions according to the learned patterns. The proposed neuro-fuzzy inference system was applied to a public yeast MGE dataset. Two simulations were conducted and checked against 112 pairs of qRT-PCR confirmed gene interactions and 77 TFs (Transcriptional Factors) pairs collected from literature respectively to evaluate the performance of the proposed algorithm.Keywords:Fuzzy inference systemGenetic networksMicroarray analysis References A. H. Tonget al., Science 303, 808 (2004), DOI: 10.1126/science.1091317. Crossref, ISI, Google Scholar Shieh G. S., Chen C. M., Yu C. Y., Huang J., Wang W. F., A stepwise structural equation modeling algorithm to reconstruct genetic networks. Technical Report C2005-04, Institute of Statistical Science, Academia Sinica, Taiwan, 2005 . Google ScholarJ. Schafer and K. Strimmer, Bioinformatics 21, 754 (2005), DOI: 10.1093/bioinformatics/bti062. Crossref, ISI, Google ScholarN. Friedmanet al., Using Bayesian network to analyze expression data, Proc. of the Fourth Annual Conf. on Research in Computational Molecular Biology (2000) pp. 127–135. Google ScholarM. J. Bealet al., Bioinformatics 21, 349 (2005), DOI: 10.1093/bioinformatics/bti014. Crossref, ISI, Google ScholarB. E. Perrinet al., Bioinformatics 19(), ii138 (2003), DOI: 10.1093/bioinformatics/btg1071. ISI, Google ScholarS. Kimuraet al., Bioinformatics 21, 1154 (2005), DOI: 10.1093/bioinformatics/bti071. Crossref, ISI, Google ScholarG. S. Shiehet al., A Regression Approach to Reconstruct Gene Networks, Proc. of 2004 Taipei Symp. on Statistical Genome (2004) pp. 357–370. Google ScholarG. Lesageet al., Genetics 167, 35 (2004), DOI: 10.1534/genetics.167.1.35. Crossref, ISI, Google ScholarR. A. Kafri, Rar-Even and Y. Pilpel, Nat. Genet. 37, 295 (2005), DOI: 10.1038/ng1523. Crossref, ISI, Google ScholarS. L. Wong and F. P. Roth, Genetics 171, 829 (2005), DOI: 10.1534/genetics.105.046060. Crossref, ISI, Google ScholarA. H. Tonget al., Science 294, 2364 (2001), DOI: 10.1126/science.1065810. Crossref, ISI, Google ScholarP. T. Spellmanet al., Mol. Biol. Cell 9, 3273 (1998). Crossref, ISI, Google ScholarL. Ji and K. L. Tan, Bioinformatics 21, 509 (2005), DOI: 10.1093/bioinformatics/bti026. Crossref, ISI, Google Scholar Reis B. Y., Butte A. J., Kohane I. S., Approaching causality: Discovering time-lag correlations in genetic expression data with static and dynamic relevance networks, Proc. of RECOMB 2000, 5 . Google ScholarL. A. Zadeh, Information Sciences 9, 43 (1976), DOI: 10.1016/0020-0255(75)90017-1. Crossref, ISI, Google ScholarE. H. Mamdani, IEEE Trans. Computers 26, 1182 (1977). ISI, Google ScholarT. Takagy and M. Sugeno, IEEE Transactions on System, Man and Cybernetics 15, 116 (1985). ISI, Google ScholarJ. S. Jang, IEEE Transactions on System, Man and Cybernetics 23, 665 (1993), DOI: 10.1109/21.256541. Crossref, ISI, Google Scholar Remember to check out the Most Cited Articles! Notable Biomedical TitlesAuthors from Harvard, Rutgers University, University College London and more! FiguresReferencesRelatedDetailsCited By 3Identification of disease genes and assessment of eye-related diseases caused by disease genes using JMFC and GDLNNSamar Jyoti Saikia and S. R. Nirmala12 August 2021 | Computer Methods in Biomechanics and Biomedical Engineering, Vol. 25, No. 4A robust correlation estimator and nonlinear recurrent model to infer genetic interactions in Saccharomyces cerevisiae and pathways of pulmonary disease in Homo sapiensCheng-Long Chuang, Chung-Ming Chen, Wai-Si Wong, Kun-Nan Tsai and Err-Cheng Chan et al.1 Dec 2009 | Biosystems, Vol. 98, No. 3Uncovering transcriptional interactions via an adaptive fuzzy logic approachCheng-Long Chuang, Kenneth Hung, Chung-Ming Chen and Grace S Shieh6 December 2009 | BMC Bioinformatics, Vol. 10, No. 1 Recommended Vol. 19, No. 02 Metrics History Accepted 5 June 2007 KeywordsFuzzy inference systemGenetic networksMicroarray analysisPDF download