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Ina Hoeschele
Researcher at Virginia Tech
Publications - 88
Citations - 3991
Ina Hoeschele is an academic researcher from Virginia Tech. The author has contributed to research in topics: Quantitative trait locus & Population. The author has an hindex of 35, co-authored 85 publications receiving 3744 citations. Previous affiliations of Ina Hoeschele include University of New England (Australia) & Technische Universität München.
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Discovery of meaningful associations in genomic data using partial correlation coefficients
TL;DR: This work proposes to use a partial correlation analysis to construct approximate Undirected Dependency Graphs from large-scale biochemical data, thereby inferring the underlying network topology of biochemical compounds.
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Mapping quantitative trait loci for milk production and health of dairy cattle in a large outbred pedigree.
Qin Zhang,Didier Boichard,Ina Hoeschele,C. A. Ernst,André Eggen,B. Murkve,Margaret Pfister-Genskow,LaRee A. Witte,F. Grignola,Pekka Uimari,G. Thaller,Michael D. Bishop +11 more
TL;DR: Quantitative trait loci affecting milk production and health of dairy cattle were mapped in a very large Holstein granddaughter design, and some chromosomes showed some evidence for 2 linked QTL affecting the same trait.
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Age-related variations in the methylome associated with gene expression in human monocytes and T cells
Lindsay M. Reynolds,Jackson Taylor,Jingzhong Ding,Kurt Lohman,W. Craig Johnson,David Siscovick,Gregory L. Burke,Wendy Post,Steven Shea,David R. Jacobs,Hendrik G. Stunnenberg,Stephen B. Kritchevsky,Ina Hoeschele,Charles E. McCall,David M. Herrington,Russell P. Tracy,Yongmei Liu +16 more
TL;DR: Potentially functional age-dMS, defined as age- and cis-gene expression-associated methylation sites (age-eMS), are identified by integrating genome-wide CpG methylation and gene expression profiles collected ex vivo from circulating T cells and monocytes.
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Gene Network Inference via Structural Equation Modeling in Genetical Genomics Experiments
TL;DR: The goal is gene network inference in genetical genomics or systems genetics experiments by constructing an encompassing directed network (EDN) and proposing structural equation modeling (SEM), because it can model cyclic networks and the EDN indeed contains feedback relationships.
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Genetical genomics analysis of a yeast segregant population for transcription network inference.
Nan Bing,Ina Hoeschele +1 more
TL;DR: An initial implementation of an analysis strategy with several steps is applied to a segregating yeast population, finding that one or several biological processes were statistically significantly overrepresented in independent network structures or in highly interconnected subnetworks.