کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
552574 | 873247 | 2007 | 19 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Large-scale regulatory network analysis from microarray data: modified Bayesian network learning and association rule mining
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
سیستم های اطلاعاتی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
We present two algorithms for learning large-scale gene regulatory networks from microarray data: a modified information-theory-based Bayesian network algorithm and a modified association rule algorithm. Simulation-based evaluation using six datasets indicated that both algorithms outperformed their unmodified counterparts, especially when analyzing large numbers of genes. Both algorithms learned about 20% (50% if directionality and relation type were not considered) of the relations in the actual models. In our empirical evaluation based on two real datasets, domain experts evaluated subsets of learned relations with high confidence and identified 20–30% to be “interesting” or “maybe interesting” as potential experiment hypotheses.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Decision Support Systems - Volume 43, Issue 4, August 2007, Pages 1207–1225
Journal: Decision Support Systems - Volume 43, Issue 4, August 2007, Pages 1207–1225
نویسندگان
Zan Huang, Jiexun Li, Hua Su, George S. Watts, Hsinchun Chen,