13 citations · 28 across the 6 of their papers we have counts for
6 papers
An ELIXIR scoping review on domain-specific evaluation metrics for synthetic data in life sciences
Styliani-Christina Fragkouli, Somya Iqbal, Lisa Crossman +11
Synthetic data has emerged as a powerful resource in life sciences, offering solutions for data scarcity, privacy protection and accessibility constraints. By creating artificial 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…
Synthetic data: How could it be used for infectious disease research?
Styliani-Christina Fragkouli, Dhwani Solanki, Leyla J Castro +4
Over the last three to five years, it has become possible to generate machine learning synthetic data for healthcare-related uses. However, concerns have been raised about potentia…
Deep Residual Error and Bag-of-Tricks Learning for Gravitational Wave Surrogate Modeling
Styliani-Christina Fragkouli, Paraskevi Nousi, Nikolaos Passalis +3
Deep learning methods have been employed in gravitational-wave astronomy to accelerate the construction of surrogate waveforms for the inspiral of spin-aligned black hole binaries,…
Autoencoder-driven Spiral Representation Learning for Gravitational Wave Surrogate Modelling
Paraskevi Nousi, Styliani-Christina Fragkouli, Nikolaos Passalis +5
Recently, artificial neural networks have been gaining momentum in the field of gravitational wave astronomy, for example in surrogate modelling of computationally expensive wavefo…