- 期刊
Mining Fuzzy Association Patterns in Gene Expression Databases
並列摘要
In this paper, we propose two fuzzy data mining approaches for microarray analysis, namely Fuzzy Associative Gene Expression (FAGE) and Ripple Effective Gene Expression Rule (REGER) algorithms. Both of them first transform microarray data into fuzzy items, and then use fuzzy operators and specially-designed data structures to discover the relationships among genes. Through the proposed algorithms, a novel pattern named Ripple Pattern is discovered that indicates the genes active at the same time with their linguistic terms being monotone increasing or decreasing. The experimental results show that the proposed algorithms are effective in discovering novel and useful rules from microarray data.
被引用紀錄
彭治平（2010）。運用於車載網路中群組平均預估行車時間演算法〔碩士論文，中原大學〕。華藝線上圖書館。https://doi.org/10.6840/cycu201000466
陳錦木（2013）。重瓣日日春之花芽形態、花形遺傳及育種〔博士論文，國立臺灣大學〕。華藝線上圖書館。https://doi.org/10.6342/NTU.2013.01400
延伸閱讀
- Chiu, H. S. (2004). Identifying Significant Transcriptional Modules by Genetic Algorithms from Gene Expression Data [master's thesis, National Taiwan University]. Airiti Library. https://doi.org/10.6342/NTU.2004.00675
- Ling, Y. (2005). Mining Maximal Sequential Patterns in Protein Databases [master's thesis, National Taiwan University]. Airiti Library. https://doi.org/10.6342/NTU.2005.02124
- Liu, H., Zilberstein, A., Pannier, P., Fleche, F., Arendt, C., Lengauer, C., & Hahn, C. S. (2012). Evaluating Translocation Gene Fusions by SNP Array Data. Cancer Informatics, (2012), 15-27. https://doi.org/10.4137/CIN.S8026
- 劉泰佑（2004）。Mining co-regulated gene pairs from microarray data〔碩士論文，元智大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0009-0112200611330426
- Horng, J. T., Huang, H. D., Tseng, K. Y., Tsou, T. S., Liu, B. J., & Kao, C. Y. (2003). Mining Correlations of Human Gene Expression from Digital Gene Expression Profiles. Journal of Information Science and Engineering, 19(6), 909-921. https://doi.org/10.6688/JISE.2003.19.6.1