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- W1574362315 abstract "Genomic technologies will revolutionize drag discovery and development; that much is universally agreed upon. The high dimension of data from such technologies has challenged available data analytic methods; that much is apparent. To date, large‐scale data repositories have not been utilized in ways that permit their wealth of information to be efficiently processed for knowledge, presumably due in large part to inadequate analytical tools to address numerous comparisons of high‐dimensional data. In candidate gene discovery, expression comparisons are often made between two features (e.g., cancerous versus normal), such that the enumeration of outcomes is manageable. With multiple features, the setting becomes more complex, in terms of comparing expression levels of tens of thousands transcripts across hundreds of features. In this case, the number of outcomes, while enumerable, become rapidly large and unmanageable, and scientific inquiries become more abstract, such as “which one of these (compounds, st..." @default.
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- W1574362315 date "2007-01-01" @default.
- W1574362315 modified "2023-09-23" @default.
- W1574362315 title "From Ambiguities to Insights: Query-based Comparisons of High-Dimensional Data" @default.
- W1574362315 doi "https://doi.org/10.1063/1.2816635" @default.
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