1 citations · 1 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…
Improving Interoperability among Defence and National Security Ontologies: Analysis and Evaluation Tasks
Jonathon Dilworth, Pedro Giesteira Cotovio, David Herron +4
The use of ontologies and knowledge graphs is becoming increasingly widespread in the defence and national security domain. Numerous ontologies have been developed through initiati…
Reinforcement-guided generative protein language models enable de novo design of highly diverse AAV capsids
Lucas Ferraz, Ana F. Rodrigues, Pedro Giesteira Cotovio +5
Adeno-associated viral (AAV) vectors are widely used delivery platforms in gene therapy, and the design of improved capsids is key to expanding their therapeutic potential. A centr…
Exploring the limits of pre-trained embeddings in machine-guided protein design: a case study on predicting AAV vector viability
Ana F. Rodrigues, Lucas Ferraz, Laura Balbi +2
Effective representations of protein sequences are widely recognized as a cornerstone of machine learning-based protein design. Yet, protein bioengineering poses unique challenges…
What can knowledge graph alignment gain with Neuro-Symbolic learning approaches?
Pedro Giesteira Cotovio, Ernesto Jimenez-Ruiz, Catia Pesquita
Knowledge Graphs (KG) are the backbone of many data-intensive applications since they can represent data coupled with its meaning and context. Aligning KGs across different domains…