CRIT Framework for Identifying Cross Patterns in Systems Biology
Author Information
Author(s): Tara A. Gianoulis, Ashish Agarwal, Michael Snyder, Mark B. Gerstein
Primary Institution: Yale University
Hypothesis
Can the CRIT framework effectively identify cross patterns in genomic data?
Conclusion
The CRIT framework successfully identifies significant cross patterns between different types of genomic data.
Supporting Evidence
- CRIT identified 13 significant cross patterns relating properties of transcription factors and their targets.
- The method was applied to breast cancer gene expression and chemogenomics data.
- CRIT allows for the integration of datasets that do not share a common index.
Takeaway
The CRIT method helps scientists find connections between different types of biological data, like drugs and proteins, to understand how they interact.
Methodology
The CRIT framework integrates multiple datasets with different indices to identify cross patterns through a series of statistical tests.
Potential Biases
Potential biases may arise from the selection of datasets and the statistical methods used.
Limitations
The method's effectiveness may vary depending on the quality and completeness of the input data.
Statistical Information
P-Value
p<0.0001
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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