6 citations · 6 across the 6 of their papers we have counts for
7 papers
Making Models That Matter: How to Build Trustworthy and Useful Systems Biology Models
John M. Hancock, Mihail Anton, Frank T. Bergmann +23
Computational models supporting mechanistic understanding of (complex) biological systems, systems behaviour prediction, and experimental design are becoming more and more embedded…
APICURON: a reactive infrastructure for credit attribution across distributed research data ecosystems
Adel Bouhraoua, Mehdi Zoubiri, Gavin Farrell +11
Data-driven biology relies on structured knowledge generated by expert biocurators, yet this work remains largely unrecognized in traditional academic assessments. To bridge this g…
Traceable Trust for action-ready artificial intelligence in bioscience
Huayu Xin, Yizhi Cai, Mukilan Deivarajan Suresh +9
Artificial intelligence (AI) is becoming part of the working infrastructure of the biosciences. AI models can predict biomolecular structures, design proteins, rank variants, annot…
Hybrid Cloud Architectures for Research Computing: Applications and Use Cases
Xaver Stiensmeier, Alexander Kanitz, Jan Krüger +12
Scientific research increasingly depends on robust and scalable IT infrastructures to support complex computational workflows. With the proliferation of services provided by resear…
AI Benchmark Democratization and Carpentry
Gregor von Laszewski, Wesley Brewer, Jeyan Thiyagalingam +28
Benchmarks are a cornerstone of modern machine learning, enabling reproducibility, comparison, and scientific progress. However, AI benchmarks are increasingly complex, requiring d…
Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences (October 2025 -- Version 2)
Gavin Farrell, Eleni Adamidi, Rafael Andrade Buono +27
Artificial intelligence (AI) has recently seen transformative breakthroughs in the life sciences, expanding possibilities for researchers to interpret biological information at an…
DOME Registry: Implementing community-wide recommendations for reporting supervised machine learning in biology
Omar Abdelghani Attafi, Damiano Clementel, Konstantinos Kyritsis +17
Supervised machine learning (ML) is used extensively in biology and deserves closer scrutiny. The DOME recommendations aim to enhance the validation and reproducibility of ML resea…