Automatic disease progression prediction models require large amounts of training data, which are seldom available, especially when it comes to rare diseases. A possible solution is to integrate data from different medical centres. Nevertheless, various centres often follow diverse data collection procedures and assign different semantics to collected data. Ontologies, used as schemas for interoperable knowledge bases, represent a state-of-the-art solution to homologate the semantics and foster data integration from various sources. This work presents the BrainTeaser Ontology (BTO), an ontology that models the clinical data associated with two brain-related rare diseases (ALS and MS) in a comprehensive and modular manner. BTO assists in organizing and standardizing the data collected during patient follow-up. It was created by harmonizing schemas currently used by multiple medical centers into a common ontology, following a bottom-up approach. As a result, BTO effectively addresses the practical data collection needs of various real-world situations and promotes data portability and interoperability. BTO captures various clinical occurrences, such as disease onset, symptoms, diagnostic and therapeutic procedures, and relapses, using an event-based approach. Developed in collaboration with medical partners and domain experts, BTO offers a holistic view of ALS and MS for supporting the representation of retrospective and prospective data. Furthermore, BTO adheres to Open Science and FAIR (Findable, Accessible, Interoperable, and Reusable) principles, making it a reliable framework for developing predictive tools to aid in medical decision-making and patient care. Although BTO is designed for ALS and MS, its modular structure makes it easily extendable to other brain-related diseases, showcasing its potential for broader applicability.Database URL https://zenodo.org/records/7886998.

Cancer prevention is one of the most pressing challenges that public health needs to face. In this regard, data-driven research is central to assist medical solutions targeting cancer. To fully harness the power of data-driven research, it is imperative to have well-organized machine-readable facts into a knowledge base (KB). Motivated by this urgent need, we introduce the Collaborative Oriented Relation Extraction (CORE) system for building KBs with limited manual annotations. CORE is based on the combination of distant supervision and active learning paradigms and offers a seamless, transparent, modular architecture equipped for large-scale processing. We focus on precision medicine and build the largest KB on ‘fine-grained’ gene expression–cancer associations—a key to complement and validate experimental data for cancer research. We show the robustness of CORE and discuss the usefulness of the provided KB. Database URLhttps://zenodo.org/record/7577127

Amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS) are debilitating diseases with unpredictable progression. Artificial Intelligence-based tools for modelling disease progression could significantly improve the quality of life for patients and caregivers while supporting clinicians in delivering more personalized and timely care. However, the limited availability of data hinders the development, testing, and reproducibility of such predictive tools. To address this challenge, we curated, in the context of the H2020 BRAINTEASER project, four datasets containing clinical data from a total of 2,290 ALS patients and 723 MS patients. These datasets also include environmental data and information collected through wearable devices. Unlike most existing resources, the BRAINTEASER datasets are gathered from clinical practice, offering a more accurate representation of the data that an AI progression prediction tool would encounter in real-world scenarios. In addition to manual and automated data quality checks, the research community has validated the datasets through three editions of the intelligent Disease Progression Prediction challenges held within the Conference and Labs of the Evaluation Forum (CLEF).

Nanopublishing is a paradigm enabling the representation of scientific claims in a distinctive, identifiable, citable, and reusable format, i.e., as a named graph. This approach can be applied to sentences extracted from scientific publications or triples within a Knowledge Base (KB). This way, one can track the provenance of assertions derived from a specific publication or database. However, nanopublications do not natively support multi-source scientific claims generated by aggregating different bodies of knowledge. This work extends the nanopublication model with knowledge provenance , capturing provenance information for assertions derived by an aggregation algorithm or a truth discovery process , e.g., an information extraction system aggregating several sources of knowledge to populate a Knowledge Base (KB). In these cases, provenance information cannot be attributed to a single source, but it is the result of an ensemble of evidence, that can comprehend supporting and conflicting pieces of evidence and truth values. Knowledge provenance is represented as a named graph following the PROV-K ontology, developed for the case. To show how knowledge provenance applies to a real-world scenario, we serialized gene expression-cancer associations generated by the Collaborative Oriented Relation Extraction (CORE) System. To demonstrate the value of trust relationships, we present a use case leveraging an existing scientific KB to construct a trust network employing three Large Language Model (LLM) agents. We analyzed the ability of LLMs to evaluate trustworthiness, exploiting techniques from KB accuracy estimation. We published 197, 511 assertions generated by the CORE system in the form of extended nanopublications with knowledge provenance. PROV-K also defines trust relationships between agents or between an agent and a proposition. Starting from these assertions, we leveraged external agents – namely, multiple LLMs – to assess their trusted truth value. Based on these values, we defined trust relationships between the agents and the facts, yielding an exemplar trust network comprising over 45,000 facts and four agents. The knowledge provenance graph allows the tracking of provenance for each piece of evidence contributing to the support or refutation of an assertion. To capture the semantics of the newly presented graph, we define the PROV-K ontology, designed to represent provenance information for multi-source assertions. The two use cases serve as a template to show how to serialize extended nanopublications and showcase the trust relationships’ capabilities.