Hide metadata

dc.contributor.authorAgibetov, Asan
dc.contributor.authorJiménez-Ruiz, Ernesto
dc.contributor.authorOndrésik, Marta
dc.contributor.authorSolimando, Alessandro
dc.contributor.authorBanerjee, Imon
dc.contributor.authorGuerrini, Giovanna
dc.contributor.authorCatalano, Chiara E.
dc.contributor.authorOliveira, Joaquim M.
dc.contributor.authorPatanè, Giuseppe
dc.contributor.authorReis, Rui L.
dc.contributor.authorSpagnuolo, Michela
dc.date.accessioned2018-02-13T06:02:30Z
dc.date.available2018-02-13T06:02:30Z
dc.date.issued2018
dc.identifier.citationJournal of Biomedical Semantics. 2018 Feb 08;9(1):9
dc.identifier.urihttp://hdl.handle.net/10852/60054
dc.description.abstractBackground Pathogenesis of inflammatory diseases can be tracked by studying the causality relationships among the factors contributing to its development. We could, for instance, hypothesize on the connections of the pathogenesis outcomes to the observed conditions. And to prove such causal hypotheses we would need to have the full understanding of the causal relationships, and we would have to provide all the necessary evidences to support our claims. In practice, however, we might not possess all the background knowledge on the causality relationships, and we might be unable to collect all the evidence to prove our hypotheses. Results In this work we propose a methodology for the translation of biological knowledge on causality relationships of biological processes and their effects on conditions to a computational framework for hypothesis testing. The methodology consists of two main points: hypothesis graph construction from the formalization of the background knowledge on causality relationships, and confidence measurement in a causality hypothesis as a normalized weighted path computation in the hypothesis graph. In this framework, we can simulate collection of evidences and assess confidence in a causality hypothesis by measuring it proportionally to the amount of available knowledge and collected evidences. Conclusions We evaluate our methodology on a hypothesis graph that represents both contributing factors which may cause cartilage degradation and the factors which might be caused by the cartilage degradation during osteoarthritis. Hypothesis graph construction has proven to be robust to the addition of potentially contradictory information on the simultaneously positive and negative effects. The obtained confidence measures for the specific causality hypotheses have been validated by our domain experts, and, correspond closely to their subjective assessments of confidences in investigated hypotheses. Overall, our methodology for a shared hypothesis testing framework exhibits important properties that researchers will find useful in literature review for their experimental studies, planning and prioritizing evidence collection acquisition procedures, and testing their hypotheses with different depths of knowledge on causal dependencies of biological processes and their effects on the observed conditions.
dc.language.isoeng
dc.rightsThe Author(s); BioMed Central Ltd.
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleSupporting shared hypothesis testing in the biomedical domain
dc.typeJournal article
dc.date.updated2018-02-13T06:02:31Z
dc.creator.authorAgibetov, Asan
dc.creator.authorJiménez-Ruiz, Ernesto
dc.creator.authorOndrésik, Marta
dc.creator.authorSolimando, Alessandro
dc.creator.authorBanerjee, Imon
dc.creator.authorGuerrini, Giovanna
dc.creator.authorCatalano, Chiara E.
dc.creator.authorOliveira, Joaquim M.
dc.creator.authorPatanè, Giuseppe
dc.creator.authorReis, Rui L.
dc.creator.authorSpagnuolo, Michela
dc.identifier.cristin1515493
dc.identifier.doihttp://dx.doi.org/10.1186/s13326-018-0177-x
dc.identifier.urnURN:NBN:no-62720
dc.type.documentTidsskriftartikkel
dc.type.peerreviewedPeer reviewed
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/60054/1/13326_2018_Article_177.pdf
dc.type.versionPublishedVersion
cristin.articleid9


Files in this item

Appears in the following Collection

Hide metadata

Attribution 4.0 International
This item's license is: Attribution 4.0 International