Inferring Gene Dependency Networks from Genomic Longitudinal Data: a Functional Data Approach

Autor: Rainer Opgen-Rhein, Korbinian Strimmer

Publikováno v: Revstat Statistical Journal, Vol 4, Iss 1 (2006)

A key aim of systems biology is to unravel the regulatory interactions among genes and gene products in a cell. Here we investigate a graphical model that treats the observed gene expression over time as realizations of random curves. This approach i

Externí odkaz: https://doaj.org/article/d3463848ebe942bcb943597624c1e933

Inference of demographic history from genealogical trees using reversible jump Markov chain Monte Carlo

Autor: Rainer, Opgen-Rhein, Ludwig, Fahrmeir, Korbinian, Strimmer

Publikováno v: Opgen-Rhein, R, Fahrmeir, L & Strimmer, K 2005, ' Inference of demographic history from genealogical trees using reversible jump Markov chain Monte Carlo ', BMC Evolutionary Biology, vol. 5, no. 6 . https://doi.org/10.1186/1471-2148-5-6
BMC Evolutionary Biology, Vol 5, Iss 1, p 6 (2005)
BMC Evolutionary Biology

Background Coalescent theory is a general framework to model genetic variation in a population. Specifically, it allows inference about population parameters from sampled DNA sequences. However, most currently employed variants of coalescent theory o

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmid_dedup__::7b3fc10adab72e7a09c27e50433fe08d
https://doi.org/10.1186/1471-2148-5-6

Accurate ranking of differentially expressed genes by a distribution-free shrinkage approach

Autor: Rainer Opgen-Rhein, Korbinian Strimmer

Publikováno v: ResearcherID

High-dimensional case-control analysis is encountered in many different settings in genomics. In order to rank genes accordingly, many different scores have been proposed, ranging from ad hoc modifications of the ordinary t statistic to complicated h

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0de0473cf249fea8b456b1db42ec5144
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000245335600007&KeyUID=WOS:000245335600007

From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data

Autor: Rainer Opgen-Rhein, Korbinian Strimmer

Publikováno v: BMC Systems Biology, Vol 1, Iss 1, p 37 (2007)
BMC Systems Biology

Background The use of correlation networks is widespread in the analysis of gene expression and proteomics data, even though it is known that correlations not only confound direct and indirect associations but also provide no means to distinguish bet

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::946ca8d79f5cc95590e7f944b2fbd882