Supporting shared hypothesis testing in the biomedical domain

last updated: 2018-02-10
Project IF/01285/2015 :: publications list
TitleSupporting shared hypothesis testing in the biomedical domain
Publication TypePapers in Scientific Journals
Year of Publication2018
AuthorsAgibetov A., Jiménez-Ruiz E., Ondrésik, M., Solimando A., Banerjee A., Guerrini G., Catalano C. E., Oliveira J. M., Guiseppe P., Reis R. L., and Spagnuolo M.

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.
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.
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.

JournalJournal of Biomedical Semantics
Date Published2018-02-09
PublisherBioMed Central
KeywordsBiomedical ontology, Hypothesis testing, Incomplete knowledge, Network analysis, Ontology mapings
Peer reviewedyes

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