Linked Life Data

19628006

Source:http://linkedlifedata.com/resource/pubmed/id/19628006

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2009-9-28
pubmed:abstractText
Addressing the issue of effective connectivity, this study focuses on effects of indirect connections on inferring stable causal relations: partial transfer entropy. We introduce a Granger causality measure based on a multivariate version of transfer entropy. The statistic takes into account the influence of the rest of the network (environment) on observed coupling between two given nodes. This formalism allows us to quantify, for a specific pathway, the total amount of indirect coupling mediated by the environment. We show that partial transfer entropy is a more sensitive technique to identify robust causal relations than its bivariate equivalent. In addition, we demonstrate the confounding effects of the variation in indirect coupling on the detectability of robust causal links. Finally, we consider the problem of model misspecification and its effect on the robustness of the observed connectivity patterns, showing that misspecifying the model may be an issue even for model-free information-theoretic approach.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
1872-678X
pubmed:author
pubmed:issnType
Electronic
pubmed:day
30
pubmed:volume
184
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
152-60
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Confounding effects of indirect connections on causality estimation.
pubmed:affiliation
Rotman Research Institute of Baycrest, Toronto, Canada. vasenka@gmail.com
pubmed:publicationType
Journal Article