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This study exploits the existence of hidden associations by including moderately associated SNPs.
This study examined an exclusive set of associated SNPs to avoid false associations, but excludes many hidden associations found in moderate association p-values.
We take a large set of the most strongly associated SNPs in each GWA study in order to capture more hidden associations at the cost of introducing false positives into our analysis.
While this will undoubtedly capture hidden associations, diminishing returns of these "hidden associations" are expected as one increases the p-value (Fig. 1).
As haplotype analysis can reveal hidden associations, we more closely examined the twenty-six common haplotypes within PRPF31 (Table S2).
Further work on the identification of semantic relationships should increase the effectiveness of text mining for hidden associations and potentially distinguish 'negative' associations.
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Biologists will benefit a lot by mining and discovering the hidden association information from the network.
In each loop, we order the 5080 diseases for the microRNAs, which is the hidden association.
We rule that if the disease that is the hidden association has the highest k value, then prediction is true.
This special issue discusses the most challenging issues in multiple SNPs approaches including gene-gene interaction and introduces statistical and computational methods for data mining and machine learning for revealing hidden association network of genotype-phenotype relationship.
Based on the assumption that hidden association could exist among laboratory tests, we employ a Bayesian approach to build a weighting function for scoring the correlation between a new laboratory test and a patient.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com