# A Linked Data Application for Harmonizing Heterogeneous Biomedical Information
## 2 Background and Related Work  
The research work described in this paper aims at supporting biologists, researchers, and specialists in the collection, organization, and analysis of existing biomedical data on _neuroendocrine neoplasms_ (NENs) through the definition and development of a linked data application. During the last four decades, these neoplasms have shown a 6.4-times increasing age-adjusted annual incidence [9]. NENs have been observed in almost every tissue, either in the pure endocrine organs, the nerve structures, or in the diffuse neuroendocrine system. The _World Health Organization_ has defined two groups of NENs to enable consistent management of diseases regardless of their anatomical location [10]: _neuroendocrine carcinomas_ (NECs) and _neuroendocrine tumors_ (NETs). Although NETs appear as well-differentiated neoplasms and can be categorized into three levels--G1, G2, and G3 (low, intermediate, and high grade)--NECs are poorly differentiated neoplasms with only high grade (i.e., G3). Cell grade and differentiation often depend on different factors such as mitotic count and Ki-67 cell labeling index [11]. Moreover, NECs can be also classified into small- or large-cell-type NECs. The integration of information about NENs is still quite difficult since variations depending on anatomical sites often lead to definitions different from the accepted and established ones. Heterogeneous data sources with different schemes and formats make the information retrieval and analysis process quite difficult to perform and often require the clinician to have advanced programming skills. Some projects have been proposed to integrate and harmonize biological data sources. For example, in the _Gene Expression Data Warehouse_ (GEDAW) project a data warehouse has been proposed to manage relevant information on liver gene expression data and related biomedical resources [12]. It provides the integration of gene information from several data sources, including _GenBank_ and _BioMeKe_. _Bio2 RDF_ also has the purpose of transforming heterogeneous biomedical information into linked data using semantic Web technologies [13]. It currently consists of 11 billion triples and 35 connected datasets. _Bio2 RDF_ is part of the _Life Sciences Linked Open Data_ (LSLOD), in the context of the _Linked Open Data_ initiative [14]. The _Knowledge Base of Biomedicine_ (KaBOB) integrates 18 biomedical data sources using 14 ontologies from the Open Biomedical Ontologies (OBO) initiative [15]. Such a model integrates data sources by producing a single biomedical entity for each set of data source-specific equivalent identifiers. Similarly, the _Genomic and Proteomic Knowledge Base_ (GPKB) links several biomedical data sources such as _Entrez Gene_, _UniProt_, _IntAct_, _Expasy Enzyme_, _GO_, _GOA_, _BioCyc_, _Kegg_, _Reactome_, and _OMIM_[16]. It provides a set of maintenance procedures to update the knowledge base depending on the evolutions of its sources and their consistency. The _Software for Flexible Integration of Annotation_ (SoFIA) aims at integrating omics information from several sources [17]. It relies on a minimal workflow that, given a starting goal indicated by the researchers, allows one to complete the task and return a relevant subset of information. More recently, the R package _Onassis_ has been introduced to easily associate samples from large-scale biomedical repositories to ontology-based annotations [18]. Onassis leverages NLP techniques, biomedical ontologies, and the R statistical framework to identify, relate, and analyze datasets from public repositories. In the domain of cancer research, _SysCancer_ has the aim to provide an integrated system that combines different stages of cancer studies [19]. The data warehouse can allow a multidimensional analysis of collected and integrated data meant for public access. In [20] the authors developed a cancer staging ontology based on the guidelines of the _AmericanJoint Commission on Cancer_.