Meetings
BioHackSWAT4HCLS 2025
BioHackathon Europe 2024
3rd BioHackathon Germany
DBCLS BioHackathon 2024
ELIXIR INTOXICOM
Recent preprints
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Software Quality Indicators: extraction, categorisation andrecommendations from canonical sources
Research software plays a central role in modern science, and its quality is increasinglyrecognized as essential for reproducibility, sustainability, and trust. Numerous initiatives haveproposed indicators to guide quality assessment, yet these indicators are dispersed acrossdomains and vary in scope, terminology, and practical use. This work presents a curatedcatalogue of software quality indicators tailored to the needs of research software. Developedduring BioHackathon Europe 2024 and refined in collaboration with the ELIXIR Tools Platformand EVERSE project, the catalogue consolidates and structures indicators from a range ofauthoritative sources. -
Addressing Background Genomic and Environmental Effects on Health through Accelerated Computing and Machine Learning: Results from the 2025 Hackathon at Carnegie Mellon University
In March 2025, 34 scientists from the United States, Ireland, the United Kingdom, Switzerland,France, Germany, Spain, India, and Australia gathered in Pittsburgh, Pennsylvania and virtuallyfor a collaborative biohackathon, hosted by DNAnexus and Carnegie Mellon University Libraries.The goal of the hackathon was to explore machine learning approaches for multimodalproblems in computational biology using public datasets. Teams worked on the followinginnovative projects: applying machine learning techniques for clustering and similarity analysisof haplotypes; adapting the StructLMM framework to study Gene-Gene (GxG) interactions;creating a nextflow workflow for generating an imputation reference panel using large-scalecohort data; optimizing discovery of causal relationships in large electronic health record (EHR)datasets using the open source causal analysis software Tetrad; examining the evolution of agraph neural network in a Lenski-esque experiment; and developing tools and workflows forgenerating pathway intersection diagrams and graph-based analyses for multiomics data. Allprojects were dedicated to study the background genomic and environmental effects underlyingcomplex genotype-phenotype relationships. Their objective was to set foundations for furtherstudies on predicting complex phenotypic traits using integrative multi-omic and environmentalanalyses. -
Leveraging RDF and CURIE metadata resolution with identifiers.org
Identifiers.org provides two core services for CURIEs in life sciences. One is a registry of CURIE prefixes and URL locations that contain entries for the main life sciences datasets. The other is a resolver that allows for consistent data access using registry information to redirect to current URLs for CURIE identifiers. For this work, we aimed to expand these services to facilitate the integration of CURIE-related metadata into different contexts. The first part of this exports the registry in RDF with a SPARQL server to allow queries on the dataset. Through these, RDF-based systems can associate with registry metadata on different data collections. Allowing, for example, services that have identifiers.org URLs to collect metadata on the collection that it references. The second part expands on the existing metadata resolver to be able to collect CURIE-related metadata from different metadata providers.While the previous resolver could only collect LDJSON notations from pages, it can now be expanded to collect from any metadata provider.For this work, we implement two proof of concept retrievers, one for EBI Search, a text search engine that allows for metadata acquisition, and one for TogoID, an ID mapping service for life sciences.Finally, we gather some future tasks for identifiers.org services. -
BioHackEU24 report: Expanding FAIR database integration through elucidation and transformation of underlying graph schemas
The BioDataFuse (BDF) project aims to enhance the interoperability of biomedical data through modular integration of data from diverse life sciences resources into context-specific knowledge graphs. This paper discusses the efforts made during BioHackathon Europe 2024 to improve the FAIR (Findable, Accessible, Interoperable, and Reusable) data integration process by clarifying and transforming graph schemas. We explored tools such as VoID-generator, RDF-config, and sheXer for data schema extraction and the integration of RDF Portal data into the BDF framework. By leveraging these tools, we automated the generation of SPARQL queries, created GraphQL endpoints, and enhanced BDF’s ability to integrate new databases. Additionally, we explored the potential of large language models (LLMs) for automated reasoning and data interpretation within the BDF ecosystem. This work lays the foundation for building more efficient and standardized data models, contributing to the seamless integration of multiple biomedical databases. -
BioHackSWAT4HCLS25 report: Towards AI-Ready Datasets for the Life Sciences
At the SWAT4HCLS 2025 Hackathon, we continued our work on dataset interoperability and AI-readiness, extending our efforts from the 2024 Elixir Biohackathon. This report outlines the progress made in graph serialization, metadata embedding, and knowledge graph analysis, which further enhance machine learning workflows and data integration -
Reusable RDM Planning Environments for Trainings and Workshops: A BioHackathon Europe 2024 Report
This report provides an overview of our activities and accomplishments related to the creation of reusable RDM (Research Data Management) Planning Environments for trainings and workshops conducted during the ELIXIR BioHackathon Europe 2024. ELIXIR recognizes the critical role of effective data management planning in enabling sustainable and reproducible research outcomes. This effectiveness is achieved through the use of appropriate Data Management Planning tools, such as the Data Stewardship Wizard. The Data Stewardship Wizard is used to conduct various trainings which require instance with data which are different for each training. Goal of this project was to provide easy and effective way to prepare “recipes” for DSW Data Seeder -
Enhancing bio.tools by Semantic Literature Mining
Mining mentions of software tools in scientific literature is important for resource discovery and analysis in bioinformatics. Despite advancements in deep-learning-based natural language processing techniques, accurately identifying software mentions remains challenging due to naming ambiguities, inconsistent citation practices, and homonyms. In this study, we developed methods to enhance the bio.tools registry through integration with Europe PMC. We systematically explored three distinct article-tool relationships: direct associations, citations of associated articles, and textual mentions without explicit citations. A hybrid approach combining rule-based heuristics and machine learning was evaluated at a F1-score of 74.4% in contextual software mention disambiguation tasks. We further demonstrated the potential for mining software co-mentions and co-citations from EuropePMC, constructing interactive networks in Cytoscape to visualize relationships between tools. Leveraging bio.tools metadata significantly improved disambiguation accuracy, including for tools with generic names. In the future, we will expand annotated datasets, handle software synonyms, and make bio.tools software mentions retrievable through the Europe PMC Annotations API to enrich bio.tools with usage data, making software more findable, including for recommendation systems.
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