Identifying Genes Involved in Cyclic Processes
Author Information
Author(s): Wentao Zhao, Erchin Serpedin, Edward R Dougherty
Primary Institution: Texas A&M University
Hypothesis
Can we identify cyclic-process-involved genes by integrating gene expression analysis with prior knowledge?
Conclusion
The proposed algorithm effectively identifies potential cyclic-process-involved genes by utilizing both gene expression data and prior knowledge.
Supporting Evidence
- The algorithm identified 722 potential cell cycle genes from Saccharomyces cerevisiae.
- Biological evidence validated the roles of discovered genes in cell cycle and circadian rhythm.
- The proposed method integrates prior knowledge to enhance gene identification accuracy.
Takeaway
This study created a new way to find genes that work in cycles, like those in the cell cycle, by looking at their activity over time and using what we already know about them.
Methodology
The study used a novel algorithm that combines spectral analysis and gene distance computation based on time series microarray data.
Potential Biases
Potential biases may arise from the reliance on prior knowledge and the inherent noise in biological data.
Limitations
The algorithm may not perform well if the cell culture is not ideally synchronized or stationary.
Participant Demographics
The study focused on gene expression data from Saccharomyces cerevisiae and Drosophila melanogaster.
Statistical Information
P-Value
0.15
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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