# A Linked Data Application for Harmonizing Heterogeneous Biomedical Information
## 4 The Integration and Harmonization Process
### Layer 1b: Phenotypic Anomalies  
As introduced in Section 2, NENs are rare diseases that include heterogeneous neoplasms such as high-grade NETs in the lung, mixed medullary and follicular cell carcinomas, intrathyroid NENs with paraganglioma features, NENs of the breast, NETs in the kidney, NETs of the bladder, etc. [10]. Although a rare disease occurs in less than 1 in 2000 individuals, due to the high number of such diseases (about 8000 according to [PERSON]), it is estimated that around 4% of the European population has a rare disease diagnosis [29].  
According to [30], about 80% of rare diseases are of genetic origin. However, due to a lack of clinical and scientific knowledge, the molecular cause is unknown for about 40% of them. The second level of the proposed model already includes information on known genetic variants linked to NENs. However, despite the increasing number of identified genetic variants, their functional impact and, consequently, the connection with rare diseases is still largely unknown. Furthermore, even for diseases for which one or more causative genes have been identified, these often do not explain the totality of cases.  
This lack of knowledge often prevents patients from receiving adequate and timely care. It is estimated that specific therapies are available for less than 10% of rare diseases,  
Figure 2: Visual representation of the updated integration model.  
including NENs. For this reason, having available detailed phenotypic data combined with ever-increasing amounts of genomic data is of enormous importance to accelerate the identification of clinically actionable prognostic or therapeutic implications and to improve the understanding of rare diseases [31]. Moreover, phenotype-based genomic analysis has also been shown to improve the diagnostic rate in patients with rare diseases [32].  
Phenotypes are the observable traits of an organism. In medical contexts, however, the word phenotype is more often used to refer to some deviation from normal morphology, physiology, or behavior. A disease is commonly characterized by one or more phenotypic features which can affect all or only a subset of individuals with the disease as well as a time course over which the phenotypic features may have onset and evolve. The HPO ontology (see Section 3.2) describes a deep hierarchy of phenotypic abnormalities whereas the HOOM ontology (see Section 3.2) associates the phenotypic anomalies described in HPO with the clinical entities included in the ORDO ontology of rare diseases.  
By harmonizing the information included in HPO and HOOM with the classes and properties of the first three layers of the initial knowledge model, we were able to construct layer 1b, aimed at describing known associations between NENs and phenotypic anomalies. Figure 3 represents the main classes and relations of our model. Gray classes and bold relationships are introduced by the integration scheme of layer 1b except for the dotted classes which are projected from the previous and subsequent layers.  
The main classes of this layer are _phenotypic anomaly_, equivalent to the _phenotypic abnormality_ class from HPO (i.e., the ancestor class of all described phenotypic anomalies), and the _disease-phenotype association_ class, a subclass of the _association_ class from HOOM (describing known associations between phenotypes and clinical entities). Only associations related to diseases classified as NENs in the first layer have been considered. Furthermore, a set of properties, inherited from HOOM and described in Table 3, are linked to each association, to qualify it with information about the frequency and the diagnostic criteria, and the provenance (e.g., scientific articles or expert opinions).  
Figure 3: Relevant classes and relations of the additional layer 1b.  
It should be noted that indirect connections between genes, gene variants, and phenotypic anomalies can be inferred from the model based on information from layer 2 that associates genes and gene variants with diseases.