Transforming Heterogeneous Data into Knowledge for Personalized Treatments—A Use Case

Big data has exponentially grown in the last decade; it is expected to grow at a faster rate in the next years as a result of the advances in the technologies for data generation and ingestion. For instance, in the biomedical domain, a wide variety of methods are available for data ingestion, e.g., liquid biopsies and medical imaging, and the collected data can be represented using myriad formats, e.g., FASTQ and Nifti. In order to extract and manage valuable knowledge and insights from big data, the problem of data integration from structured and unstructured data needs to be effectively solved. In this paper, we devise a knowledge-driven approach able to transform disparate data into knowledge from which actions can be taken. The proposed framework resorts to computational extraction methods for mining knowledge from data sources, e.g., clinical notes, images, or scientific publications. Moreover, controlled vocabularies are utilized to annotate entities and a unified schema describes the meaning of these entities in a knowledge graph; entity linking methods discover links to existing knowledge graphs, e.g., DBpedia and Bio2RDF. A federated query engine enables the exploration of the linked knowledge graphs while knowledge discovery methods allow for uncovering patterns in the knowledge graphs. The proposed framework is used in the context of the EU H2020 funded project iASiS with the aim of paving the way for accurate diagnostics and personalized treatments.