Analysis on Time-lagged Gene Clusters in Time Series Gene Expression Data
Tao Zeng, Juan Liu
Abstract
Tao Zeng, Juan Liu
Abstract
There are a number of previous approaches for identifying time-lagged gene co-regulations. Popularly used cross-correlation method and edge detection method don't consider the direction of regulation of gene pairs. And event method is proposed to deal with some of the above-mentioned limitations however its scoring system to identify promising time-lagged gene pairs is still questionable. This paper further analyses the time-lagged gene clusters mining process and some probably existing deduced direction of co-regulation ' s contradictory phenomenon in these clusters/bi- clusters. Besides, the time-lagged value is proposed as a possible prevention mechanism.
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There are a number of previous approaches for identifying time-lagged gene co-regulations. Popularly used cross-correlation method and edge detection method don't consider the direction of regulation of gene pairs. And event method is proposed to deal with some of the above-mentioned limitations however its scoring system to identify promising time-lagged gene pairs is still questionable. This paper further analyses the time-lagged gene clusters mining process and some probably existing deduced direction of co-regulation ' s contradictory phenomenon in these clusters/bi- clusters. Besides, the time-lagged value is proposed as a possible prevention mechanism.
Key concepts: Time series, Gene, Series (stratigraphy), Computer science, Value (mathematics), Event (particle physics), Enhanced Data Rates for GSM Evolution, Data mining