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Top1. Introduction
The last few years have marked the explosion of data in the field of biomedicine. Several key events, such as the completion of the Human Genome Project, the advent of next-generation sequencing technologies and the Internet of Things, have led to a significant increase of the volume and variety of available biomedical data including medical records, imaging data, sequencing data, sensor data, etc. (Kamdar, Fernández, Polleres, Tudorache, & Musen, 2019).
Ontologies and open databases are widely used in biology and medicine to store this huge and ever-increasing amount of information. Unfortunately, this often results in hundreds of large, fragmented, isolated, and heterogeneous data sources, each using a different format and scheme. As a matter of fact, healthcare professionals and biomedical researchers are facing serious difficulties in finding the information they need and even in mastering the enormous amount of available data. Furthermore, it should be considered that, while some information sources are primary (i.e., they collect data directly from articles published in biomedical journals), others are the result of systematic reviews. Without a method of critical evaluation and synthesis of this information, its integration, analysis, visualization and, in other words, translation into knowledge is almost impossible.
To overcome these limitations, new tools are needed, capable of querying multiple databases behind the scenes and providing researchers with integrated biomedical information and semantically interconnected entities (Fathalla, 2018). This integration must be transparent for researchers who would no longer have to worry about finding information sources, interpreting their syntax and schemas or mapping elements to reconcile concepts, relationships, and entities (Kamdar, 2018).
The research described in this paper goes exactly in this direction, aiming at the definition and implementation of a linked data application for the analysis, aggregation and study of available data related to Neuroendocrine Neoplasms (NENs), which are relatively rare neoplasms with 6.4-times increasing age-adjusted annual incidence during the last four decades (Grigoris Effraimidis, 2021). The developed system harmonizes the way information is stored in the existing biomedical information sources, thus contributing to the interoperability between these sources and improving the work of scientists in investigating these rare diseases.
The proposed solution interconnects information sources, representing a wide spectrum of current studies and expertise, by means of a single and robust ecosystem, thus providing the researcher with a quick access point to a dense network of information. Connected information include 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.
Given the heterogeneity of interconnected information, the multilayer network formalism (implemented with semantic web languages and technologies) has been adopted to semantically link the available data sources (Hammoud & Kramer, 2020). Such networks are made up of distinct “layers” (each grouping concepts and relations), corresponding to different “aspects” of the domain, which are in turn connected with interlayer relationships. In particular, three interconnected layers have been designed that represent, respectively, information on diseases, affected genes, and biological processes and molecular functions of such genes and related gene products.
To the best of our knowledge, this is the first example of a multilayer network based on linked data and semantic web and the first tool for analyzing, aggregating, and studying data on rare tumors. By querying the system, researchers and healthcare professionals can obtain, through a user-friendly interface, answers to scientific questions such as relations between pathologies, involved genes and their mutations.
The paper is organized as follows: in section 2 the related work on biomedical data integration is summarized and the work is contextualized in the relevant literature; in section 3 background information on NENs and related biomedical data sources is provided; in section 4 the knowledge base architecture and the related integration issues are presented; in section 5 the developed prototype is described. The last section summarizes the conclusions and outlines the ongoing work.