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 ...

A privacy-first, open-source platform for knowledge management and collaboration. Download link: http://github.com/logseq/logseq/releases. roadmap: http://trello.com ...

AAAI 2024 Papers: Explore a comprehensive collection of innovative research papers presented at one of the premier artificial intelligence conferences. Seamlessly integrate code implementations for ...

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

without integrating process knowledge or guidance, are limited in generalization ability. This article proposes a graph-based self-explanatory fault diagnosis model. The model employs a graph ...

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 ...

However, we observe that the aggregation of node information in multilayer graph autoencoder (GAE) is prone to deviation, especially when edges or node attributes are randomly perturbed. To this end, ...