Identification of temporal association rules from time-series microarray data sets
作者： Hojung Nam , KiYoung Lee , Doheon Lee
DOI: 10.1186/1471-2105-10-S3-S6
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摘要: Background One of the most challenging problems in mining gene expression data is to identify how any particular affects other genes. To elucidate relationships between genes, an association rule (ARM) method has been applied microarray data. However, a conventional ARM limit on extracting temporal dependencies expressions, though information indispensable discover underlying regulation mechanisms biological pathways. In this paper, we propose novel method, referred as (TARM), which can extract among related A form [gene A↑, B↓] → (7 min) C↑], represents that high level and significant repression B followed by C after 7 minutes. The proposed TARM tested with Saccharomyces cerevisiae cell cycle time-series set.
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