In search of functional association from time-series microarray data based on the change trend and level of gene expression

Background: The increasing availability of time-series expression data opens up new possibilities to study functional linkages of genes. Present methods used to infer functional linkages between genes from expression data are mainly based on a point-to-point comparison. Change trends between consecutive time points in time-series data have been so far not well explored. Results: In this work we present a new method based on extracting main features of the change trend and level of gene expression between consecutive time points. The method, termed as trend correlation (TC), includes two major steps: 1, calculating a maximal local alignment of change trend score by dynamic programming and a change trend correlation coefficient between the maximal matched change levels of each gene pair; 2, inferring relationships of gene pairs based on two statistical extraction procedures. The new method considers time shifts and inverted relationships in a similar way as the local clustering (LC) met...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Feng He, An-Ping Zeng
• Keywords: BMCBI 2006, Change Trend, Consecutive Time Points, Gene Pairs