Statistical tests for associations between two directed acyclic graphs.

Biological data, and particularly annotation data, are increasingly being represented in directed acyclic graphs (DAGs). However, while relevant biological information is implicit in the links between multiple domains, annotations from these different domains are usually represented in distinct, unc...

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Autores principales:	Robert Hoehndorf, Axel-Cyrille Ngonga Ngomo, Michael Dannemann, Janet Kelso
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3963b90fab3f45c9963daedfb140b654
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spelling	oai:doaj.org-article:3963b90fab3f45c9963daedfb140b6542021-12-02T20:20:46ZStatistical tests for associations between two directed acyclic graphs.1932-620310.1371/journal.pone.0010996https://doaj.org/article/3963b90fab3f45c9963daedfb140b6542010-06-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20585388/?tool=EBIhttps://doaj.org/toc/1932-6203Biological data, and particularly annotation data, are increasingly being represented in directed acyclic graphs (DAGs). However, while relevant biological information is implicit in the links between multiple domains, annotations from these different domains are usually represented in distinct, unconnected DAGs, making links between the domains represented difficult to determine. We develop a novel family of general statistical tests for the discovery of strong associations between two directed acyclic graphs. Our method takes the topology of the input graphs and the specificity and relevance of associations between nodes into consideration. We apply our method to the extraction of associations between biomedical ontologies in an extensive use-case. Through a manual and an automatic evaluation, we show that our tests discover biologically relevant relations. The suite of statistical tests we develop for this purpose is implemented and freely available for download.Robert HoehndorfAxel-Cyrille Ngonga NgomoMichael DannemannJanet KelsoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 5, Iss 6, p e10996 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Robert Hoehndorf Axel-Cyrille Ngonga Ngomo Michael Dannemann Janet Kelso Statistical tests for associations between two directed acyclic graphs.
description	Biological data, and particularly annotation data, are increasingly being represented in directed acyclic graphs (DAGs). However, while relevant biological information is implicit in the links between multiple domains, annotations from these different domains are usually represented in distinct, unconnected DAGs, making links between the domains represented difficult to determine. We develop a novel family of general statistical tests for the discovery of strong associations between two directed acyclic graphs. Our method takes the topology of the input graphs and the specificity and relevance of associations between nodes into consideration. We apply our method to the extraction of associations between biomedical ontologies in an extensive use-case. Through a manual and an automatic evaluation, we show that our tests discover biologically relevant relations. The suite of statistical tests we develop for this purpose is implemented and freely available for download.
format	article
author	Robert Hoehndorf Axel-Cyrille Ngonga Ngomo Michael Dannemann Janet Kelso
author_facet	Robert Hoehndorf Axel-Cyrille Ngonga Ngomo Michael Dannemann Janet Kelso
author_sort	Robert Hoehndorf
title	Statistical tests for associations between two directed acyclic graphs.
title_short	Statistical tests for associations between two directed acyclic graphs.
title_full	Statistical tests for associations between two directed acyclic graphs.
title_fullStr	Statistical tests for associations between two directed acyclic graphs.
title_full_unstemmed	Statistical tests for associations between two directed acyclic graphs.
title_sort	statistical tests for associations between two directed acyclic graphs.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/3963b90fab3f45c9963daedfb140b654
work_keys_str_mv	AT roberthoehndorf statisticaltestsforassociationsbetweentwodirectedacyclicgraphs AT axelcyrillengongangomo statisticaltestsforassociationsbetweentwodirectedacyclicgraphs AT michaeldannemann statisticaltestsforassociationsbetweentwodirectedacyclicgraphs AT janetkelso statisticaltestsforassociationsbetweentwodirectedacyclicgraphs
_version_	1718374159530065920

Statistical tests for associations between two directed acyclic graphs.

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