The ontology of biological taxa

Motivation: The classification of biological entities in terms of species and taxa is an important endeavor in biology. Although a large amount of statements encoded in current biomedical ontologies is taxon-dependent there is no obvious or standard way for introducing taxon information into an integrative ontology architecture, supposedly because of ongoing controversies about the ontological nature of species and taxa

The ontology of biological taxa

Motivation: The classification of biological entities in terms of species and taxa is an important endeavor in biology. Although a large amount of statements encoded in current biomedical ontologies is taxon-dependent there is no obvious or standard way for introducing taxon information into an integrative ontology architecture, supposedly because of ongoing controversies about the ontological nature of species and taxa. Results: In this article, we discuss different approaches on how to represent biological taxa using existing standards for biomedical ontologies such as the description logic OWL DL and the Open Biomedical Ontologies Relation Ontology. We demonstrate how hidden ambiguities of the species concept can be dealt with and existing controversies can be overcome. A novel approach is to envisage taxon information as qualities that inhere in biological organisms, organism parts and populations

Schema extraction for privacy preserving processing of sensitive data

Sharing privacy sensitive data across organizational boundaries is commonly not a viable option due to the legal and ethical restrictions. Regulations such as the EU General Data Protection Rules impose strict requirements concerning the protection of personal data. Therefore new approaches are emerging to utilize data right in their original repositories without giving direct access to third parties, such as the Personal Health Train initiative [16]. Circumventing limitations of previous systems, this paper proposes an automated schema extraction approach compatible with existing Semantic Web-based technologies. The extracted schema enables ad-hoc query formulation against privacy sensitive data sources without requiring data access, and successive execution of that request in a secure enclave under the data provider's control. The developed approach permit us to extract structural information from non-uniformed resources and merge it into a single schema to preserve t he privacy of each data source. Initial experiments show that our approach overcomes the reliance of previous approaches on agreeing upon shared schema and encoding a priori in favor of more flexible schema extraction and introspection

ACGT: advancing clinico-genomic trials on cancer - four years of experience.

The challenges regarding seamless integration of distributed, heterogeneous and multilevel data arising in the context of contemporary, post-genomic clinical trials cannot be effectively addressed with current methodologies. An urgent need exists to access data in a uniform manner, to share information among different clinical and research centers, and to store data in secure repositories assuring the privacy of patients. Advancing Clinico-Genomic Trials (ACGT) was a European Commission funded Integrated Project that aimed at providing tools and methods to enhance the efficiency of clinical trials in the -omics era. The project, now completed after four years of work, involved the development of both a set of methodological approaches as well as tools and services and its testing in the context of real-world clinico-genomic scenarios. This paper describes the main experiences using the ACGT platform and its tools within one such scenario and highlights the very promising results obtained

A distributed analytics platform to execute FHIR-based phenotyping algorithms

Despite the benefits of reusing health data collected in routine care, sharing datasets outside of the organizational boundaries is not always possible due to the legal and ethical restrictions. The Personal Health Train (PHT) is a novel privacy-preserving approach to execute analytics tasks at distributed data repositories, without sharing the data itself. In this work, we report a proof-of-concept implementation of the PHT by using FHIR data standards and Clinical Query Language (CQL). The Semantic Web and containerization technologies have been utilized to move computations to the data. We developed tools to design phenotyping algorithms on the data consumer side, implemented an infrastructure to transfer and execute Docker containers at the data centers, and to return results to the consumers. We experimented the evaluated PHT infrastructure and tools by designing a phenotyping algorithm for diabetes mellitus and prostate cancer risk case-control study and executed it at three distributed FHIR repositories