# A Linked Data Application for Harmonizing Heterogeneous Biomedical Information
## 1 Introduction  
The massive and continuous growth of biomedical data of a heterogeneous nature requires ever greater efforts aimed at their integration. Indeed, the proliferation of non-integrated and non-interoperable data greatly hinders their interpretation and prevents computer-assisted reasoning. Data integration and reproducibility are essential to biomedical studies; it becomes extremely important to observe the FAIR guiding principles where research works are recommended to be findable, accessible, interoperable, and reusable [1]. Generally, a key requirement for allowing data to be FAIR is the use of open approaches and standardized representation formalisms such as ontologies. Indeed, ontologies have proved crucial in supporting omics data integration [2]. However, since hundreds of biomedical ontologies have been designed and are currently available, a new problem has arisen, i.e., how to integrate different ontological schemes and make them interoperable [3]. To this end, in addition to syntactic issues related to the variety of models and formats, semantic problems arising from context-related different interpretations need to be encountered [4]. To get the most out of these data, different approaches are required for querying multiple ontologies and databases to provide researchers with biomedical information organized as interconnected entities in a semantic fashion [5]. The research described in this paper attempts to contribute toward this end within a specific domain, through an ontology-based _linked data_ application. An approach to data aggregation and analysis in the field of rare _neuroendocrine neoplasms_ (NENs) is proposed. In previous research by the same authors [6] a novel linked data application was described for the same domain, integrating several existing biomedical ontologies including the _National Cancer Institute Thesaurus_, the _Mondo Disease Ontology_, the _MedGen_ database, the _Disease Ontology_, the _Orphanet Rare Disease Ontology_, the _DisGeNet_ database, and the _Gene Ontology_. Such data sources provided the relevant information to build a single knowledge model about NENs that is accessible via a client-server application. In this work, an extension of the previous model through the integration of additional data sources is proposed. The knowledge model relies on the _multilayer network_ formalism to semantically link heterogeneous data sources while grouping them into several \"layers\", each corresponding to a specific \"aspect\" of the domain [7]. The original model consisted of three interconnected layers representing: information about diseases, affected genes, biological processes, and molecular functions of such genes and related gene products. In addition to the information included in the original model, the extended version integrates information from new sources including the _Human Phenotype Ontology_, the _HPO-ORDO Ontological Module_, the _Human Protein Atlas_, the _Drug-Gene Interaction Database_ as well as the _ChEMBL database_. Starting from the previous model, three additional layers have been designed and integrated to consider further aspects. * The _first additional layer_ provides details about disease-related _phenotypes_ such as morphology, development, biochemical, physiological, and other features. It has been shown that a deep understanding of rare diseases together with the identification of prognostic and therapeutic implications can be accelerated by analyzing and correlating phenotypic and genomic data. * The _second additional layer_ provides an association between genes responsible for diseases and human tissue. This allows us to find commonalities between organs and illustrate the role of genes related to NENs. * The _third additional layer_ provides details about _drugs_ with documented interactions with genes affected by NENs.