7 citations · 11 across the 5 of their papers we have counts for
5 papers
Hakken: Predicting future discoveries to fill the gaps in today's knowledge
Tarek R. Besold, Uchenna Akujuobi, Pablo Sanchez +21
We present Hakken, a domain-agnostic prediction and explanation system performing knowledge prediction, i.e., growing scientific knowledge by establishing novel relationships, ones…
Shaping Scientific Explanations to Expert Perspectives with Persona-Conditioned Reinforcement Learning
Susana Nunes, Tiago Guerreiro, Catia Pesquita
Explainable AI is increasingly important to scientific discovery. However, existing methods largely ignore that explanation quality is not universal: experts differ in how they ass…
ECKO: Explainable Clinical Knowledge for Oncology
Marta Contreiras Silva, Daniel Faria, Laura Balbi +13
Personalized oncology aims to tailor treatment strategies to the unique molecular and clinical profiles of individual patients, moving beyond the traditional paradigm of treating t…
Rewarding Explainability in Drug Repurposing with Knowledge Graphs
Susana Nunes, Samy Badreddine, Catia Pesquita
Knowledge graphs (KGs) are powerful tools for modelling complex, multi-relational data and supporting hypothesis generation, particularly in applications like drug repurposing. How…
Predicting Gene-Disease Associations with Knowledge Graph Embeddings over Multiple Ontologies
Susana Nunes, Rita T. Sousa, Catia Pesquita
Ontology-based approaches for predicting gene-disease associations include the more classical semantic similarity methods and more recently knowledge graph embeddings. While semant…