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• W1544207610 abstract "The availability of expression quantitative trait loci (eQTL) data can help understanding the genetic basis of variation in gene expression. However, it has proven difficult to accurately predict functional genetic changes due to low statistical power. To address this challenge, we developed a novel computational approach for combining eQTL data with complementary regulatory network to identify modules of genes, their underlying genetic polymorphism and their shared regulatory proteins activity. The resulting eQTL model implicates novel central protein complexes that share not only a regulatory protein but also an underlying genetic variation. Our method manifests higher sensitivity than prior computational efforts." @default.
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• W1544207610 date "2011-01-01" @default.
• W1544207610 modified "2023-09-23" @default.
• W1544207610 title "Understanding Gene Sequence Variation in the Context of Transcription Regulation in Yeast" @default.
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• W1544207610 doi "https://doi.org/10.1007/978-3-642-20036-6_8" @default.
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