Converting Alzheimer’s Disease Map into a Heavyweight Ontology: A Formal Network to Integrate Data

  • Vincent Henry
  • Ivan Moszer
  • Olivier Dameron
  • Marie-Claude Potier
  • Martin Hofmann-Apitius
  • Olivier Colliot
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11371)


Alzheimer’s disease (AD) pathophysiology is still imperfectly understood and current paradigms have not led to curative outcome. Omics technologies offer great promises for improving our understanding and generating new hypotheses. However, integration and interpretation of such data pose major challenges, calling for adequate knowledge models. AlzPathway is a disease map that gives a detailed and broad account of AD pathophysiology. However, AlzPathway lacks formalism, which can lead to ambiguity and misinterpretation. Ontologies are an adequate framework to overcome this limitation, through their axiomatic definitions and logical reasoning properties. We introduce the AD Map Ontology (ADMO), an ontological upper model based on systems biology terms. We then propose to convert AlzPathway into an ontology and to integrate it into ADMO. We demonstrate that it allows one to deal with issues related to redundancy, naming, consistency, process classification and pathway relationships. Further, it opens opportunities to expand the model using elements from other resources, such as generic pathways from Reactome or clinical features contained in the ADO (AD Ontology). A version of ADMO is freely available at


Alzheimer’s disease Ontology Disease map Model consistency 



The research leading to these results has received funding from the program “Investissements d’avenir” ANR-10-IAIHU-06 (Agence Nationale de la Recherche-10-IA Institut Hospitalo-Universitaire-6) and from the Inria Project Lab Program (project Neuromarkers).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.InriaParisFrance
  2. 2.ICM, Inserm U1127, CNRS UMR 7225, Sorbonne UniversitéParisFrance
  3. 3.Univ Rennes, CNRS, Inria, IRISA - UMR 6074RennesFrance
  4. 4.Fraunhofer SCAISankt AugustinGermany
  5. 5.Department of Neurology and NeuroradiologyAP-HP, Pitié-Salpêtrière HospitalParisFrance

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