Graph out-of-distribution (OOD) generalization remains a major challenge in graph neural networks (GNNs). Invariant learning, aiming to extract invariant features across varied distributions, has ...

Text mining and knowledge graphs connect cell-culture parameters to glycosylation for faster bioprocess optimization.

Use AI tools to build apps without coding. This guide covers setup, limits, risks, and SEO tool examples to inspire your own ...

These versatile strategies—from brain dumps to speed sharing—help students track their own progress while informing your next instructional steps.

Background: Biomedical knowledge graphs (KGs), such as the Data Distillery Knowledge Graph (DDKG), capture known relationships among entities (e.g., genes, diseases, proteins), providing valuable ...

Knowledge graphs are a powerful tool for bringing together information from biological databases and linking what is already known about genes, diseases, treatments, molecular pathways and symptoms in ...

What if you could transform overwhelming, disconnected datasets into a living, breathing map of relationships, one that not only organizes your data but also reveals insights you didn’t even know you ...

AI code generation for accelerating software development is gaining significant attention these days. However, this could lead to a growing bottleneck in managing all aspects that occur after ...

Abstract: The integration of knowledge graphs into industrial product development workflows offers a promising solution for managing complex and heterogeneous data to enhance development efficiency.

A TechRadar article noted that nearly 90% of enterprise information (documents, emails, videos) lies dormant in unstructured systems. This "dark data" isn't just neglected; it's a liability. GenAI ...

Many US government agencies and organizations are involved in countering weapons of mass destruction (CWMD). While overall goals and specific mission objectives vary, the US government typically ...