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- aggregation classification "A1".
- aggregation creator B313685.
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation date "2013".
- aggregation format "application/pdf".
- aggregation hasFormat 3199580.bibtex.
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- aggregation hasFormat 3199580.doc.
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- aggregation isPartOf urn:issn:1367-4803.
- aggregation language "eng".
- aggregation rights "I have transferred the copyright for this publication to the publisher".
- aggregation subject "Technology and Engineering".
- aggregation title "EPSILON: an eQTL prioritization framework using similarity measures derived from local networks".
- aggregation abstract "Motivation: When genomic data are associated with gene expression data, the resulting expression quantitative trait loci (eQTL) will likely span multiple genes. eQTL prioritization techniques can be used to select the most likely causal gene affecting the expression of a target gene from a list of candidates. As an input, these techniques use physical interaction networks that often contain highly connected genes and unreliable or irrelevant interactions that can interfere with the prioritization process. We present EPSILON, an extendable framework for eQTL prioritization, which mitigates the effect of highly connected genes and unreliable interactions by constructing a local network before a network-based similarity measure is applied to select the true causal gene. Results: We tested the new method on three eQTL datasets derived from yeast data using three different association techniques. A physical interaction network was constructed, and each eQTL in each dataset was prioritized using the EPSILON approach: first, a local network was constructed using a k-trials shortest path algorithm, followed by the calculation of a network-based similarity measure. Three similarity measures were evaluated: random walks, the Laplacian Exponential Diffusion kernel and the Regularized Commute-Time kernel. The aim was to predict knockout interactions from a yeast knockout compendium. EPSILON outperformed two reference prioritization methods, random assignment and shortest path prioritization. Next, we found that using a local network significantly increased prioritization performance in terms of predicted knockout pairs when compared with using exactly the same network similarity measures on the global network, with an average increase in prioritization performance of 8 percentage points (P < 10(-5)).".
- aggregation authorList BK601949.
- aggregation endPage "1316".
- aggregation issue "10".
- aggregation startPage "1308".
- aggregation volume "29".
- aggregation aggregates 3199596.
- aggregation isDescribedBy 3199580.
- aggregation similarTo btt142.
- aggregation similarTo LU-3199580.