Linked Life Data

20375455

Source:http://linkedlifedata.com/resource/pubmed/id/20375455

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2010-4-8
pubmed:abstractText
Many methods for the analysis of gene expression-, protein- or metabolite-data focus on the investigation of binary relationships, while the underlying biological processes creating this data may generate relations of higher than bivariate complexity. We give a novel method ExPlanes that helps to explore certain types of ternary relationships in a statistically robust, Bayesian framework. To arrive at an characterization of the data structure contained in triplet data we investigate 2-dimensional planes being the only linear structures that cannot be inferred from projections of the data. The key part of our methodology is the definition of a robust, Bayesian plane posterior under the assumption of an invariant prior and a Gaussian error model. A numerical representation of the plane posterior can be explored interactively. Beyond this purely Bayesian approach we can use the plane posterior to construct a family of posterior-based test statistics that allow testing the data for different plane related hypotheses. To demonstrate practicability we queried triplets of metabolic data from a plant crossing experiment for the presence of plane-, line- and point-structures by using posterior-based test statistics and were able to show their distinctiveness.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1613-4516
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
7
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
ExPlanes: exploring planes in triplet data.
pubmed:affiliation
Institut für Mathematik, Angewandte Sekr Holschneider, Universität Potsdam, Campus Golm, Building 28, Karl-Liebknecht-Str 24 D-14476 Potsdam.
pubmed:publicationType
Journal Article