Applied multivariate statistical analysis
Applied multivariate statistical analysis
Analysis techniques for microarray time-series data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Time-frequency feature detection for time-course microarray data
Proceedings of the 2004 ACM symposium on Applied computing
A Time Series Analysis of Microarray Data
BIBE '04 Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering
Analyzing time series gene expression data
Bioinformatics
Discover Gene Specific Local Co-regulations Using Progressive Genetic Algorithm
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Gene Specific Co-regulation Discovery: An Improved Approach
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
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Discovering gene co-regulatory relationships is a new yet important research problem in DNA microarray data analysis. The problem of gene specific co-regulation discovery is to, for a particular gene of interest, called the target gene, identify its strongly co-regulated genes from the database and the experimental condition subsets where such strong gene co-regulations are observed. The study on this problem can contribute to a better understanding and characterization of the target gene. The existing technique, mainly using genetic algorithm (GA) to discover co-regulation conditional subsets, is slow due to its expensive fitness evaluation and long solution encoding scheme. In this paper, we propose a novel technique to improve the performance of gene specific co-regulation discovery using a bit freezing approach. Through freezing converged bits in the solution encoding strings, this innovative approach can contribute to fast crossover and mutation operations, achieve an early stop of the GA and facilitate the construction of kNN Search Table Plus (kNN-ST+) that leads to more accurate approximation of fitness function. Experimental results with a real-life gene microarray data set demonstrate the improved efficiency of our technique compared with the existing method.