Many studies have used single-cell RNA sequencing (scRNA-seq) to infer gene regulatory networks (GRNs), which are crucial for ...

The aim of the workshop is to connect researchers working on this topic and surface novel research ideas and collaboration ...

With AI creating a need for knowledge engineering, information science (as we used to call it) has come full circle. It comes ...

However, domain-specific approaches for extracting knowledge graph representations from semantic information remain limited. In this paper, we develop a natural language processing (NLP) approach to ...

More information: Xiaorui Su et al, Interpretable identification of cancer genes across biological networks via transformer-powered graph representation learning, Nature Biomedical Engineering (2025).

Knowledge graphs have existed for a long time and have proven valuable across social media sites, cultural heritage institutions, and other enterprises. A knowledge graph is a collection of ...

This process is detailed in our recent publication, QirK: Question Answering via Intermediate Representation on Knowledge Graphs ... high-value machine learning and AI solutions across various ...

Abstract: Knowledge Graphs (KGs) are data structures that enable the integration of heterogeneous data sources and supporting both knowledge representation and formal ... symbolic reasoning with ...

The practices behind these projects have transformed libraries from simple book repositories into dynamic community centres that promote learning, communication and collaboration. Nestled into the ...

Question Decomposition Meaning Representation, and Optimization by Prompting. We evaluate these six prompting methods on the newly created Spider4SPARQL benchmark, as it is the most complex ...