Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Supporting shared hypothesis testing in the biomedical domain
Background: 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 ; This work was partially funded by the EU Marie Curie, ITN MultiScaleHuman (FP7-PEOPLE-2011-ITN, Grant agreement no.: 289897), the CNR project DIT.AD009.006 Modelling and Analysis of anatomical shapes for computer assisted diagnosis, the BIGMED project (IKT 259055), the HealthInsight project (IKT 247784), the SIRIUS Centre for Scalable Data Access (Research Council of Norway, project no.: 237889), the program Investigador of the Portuguese Foundation of Science and Technology (FCT, IF/00423/2012), the EU project Optique (FP7-ICT-318338), and the EPSRC projects ED3 and DBOnto. ; info:eu-repo/semantics/publishedVersion
Supporting shared hypothesis testing in the biomedical domain
Background: 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 ; This work was partially funded by the EU Marie Curie, ITN MultiScaleHuman (FP7-PEOPLE-2011-ITN, Grant agreement no.: 289897), the CNR project DIT.AD009.006 Modelling and Analysis of anatomical shapes for computer assisted diagnosis, the BIGMED project (IKT 259055), the HealthInsight project (IKT 247784), the SIRIUS Centre for Scalable Data Access (Research Council of Norway, project no.: 237889), the program Investigador of the Portuguese Foundation of Science and Technology (FCT, IF/00423/2012), the EU project Optique (FP7-ICT-318338), and the EPSRC projects ED3 and DBOnto. ; info:eu-repo/semantics/publishedVersion
Supporting shared hypothesis testing in the biomedical domain
Agibetov, Asan (Autor:in) / Jimenez-Ruiz, Ernesto (Autor:in) / Ondrésik, M. M. (Autor:in) / Solimando, Alessandro (Autor:in) / Banerjee, Imon (Autor:in) / Guerrini, Giovanna (Autor:in) / Catalano, Chiara E. (Autor:in) / Oliveira, Joaquim M. (Autor:in) / Patane, Giuseppe (Autor:in) / Reis, R. L. (Autor:in)
08.02.2018
29422110
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DDC:
690
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