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Naïve Bayesian models assume independence between all input datasets, which can artificially inflate predicted probabilities when this assumption is violated (e.g. when multiple very similar datasets are integrated).
Diverse types of whole genome datasets are integrated with the functional knowledgebase and basic statistical and machine learning procedures into a comprehensive data mining environment.
For cases in which a relatively manageable number of site-level datasets are integrated, merging can often be done manually and well informed quality control and assurance can be completed using expert knowledge of individual datasets.
These datasets are integrated alongside complementary full-data collections containing metabolic disease-relevant subsets, e.g. metabolic pathways from KEGG and Reactome, the Genome-Wide Assocontainingudies (GWAS) Catalogue (containing various type II diabetes, metabolism and obesity studies) and gene expression data from studies profiling metabolic disease-relevant tissues and subjects.
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All datasets were integrated into a Geographic Information System.
For this experiment, eight list datasets were integrated.
It was demonstrated that the expression profiles had higher specificity after datasets were integrated.
The native and SAD datasets were integrated with MOSFLM [ 33] and scaled with SCALA [ 34].
> -wrap-foot> The source datasets were integrated to form a combined Ondex Saccharomyces knowledge network.
The two datasets were integrated with iMOSFLM [ 30] and merged and scaled with SCALA [ 31].
Both datasets were integrated by transcript discovery analysis to identify putative genes and genome contigs were screened for homology.
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