Abstract: In the field of graph self-supervised learning (GSSL), graph autoencoders and graph contrastive learning are two mainstream methods. Graph autoencoders aim to learn representations by ...

Many successful machine learning models for molecular property prediction rely on Lewis structure representations, commonly encoded as SMILES strings. However, a key limitation arises with molecules ...

Graphs and data visualizations are all around us—charting our steps, our election results, our favorite sports teams’ stats, and trends across our world. But too often, people glance at a graph ...

In this tutorial, we’ll show how to create a Knowledge Graph from an unstructured document using an LLM. While traditional NLP methods have been used for extracting entities and relationships, Large ...

Introduction: Predicting interactions between microRNAs (miRNAs) and messenger RNAs (mRNAs) is crucial for understanding gene expression regulation mechanisms and their roles in diseases. Existing ...

What if you could transform vast amounts of unstructured text into a living, breathing map of knowledge—one that not only organizes information but reveals hidden connections you never knew existed?

The growing availability of spatial transcriptomics data offers key resources for annotating query datasets using reference datasets. However, batch effects, unbalanced reference annotations, and ...

A Knowledge Graph Memory Server allows Claude Desktop to remember and organize information about a user across multiple chats. It can store things like user preferences, past conversations, and ...

Abstract: Program comprehension is a fundamental activity in the field of software engineering. However, efficiently and accurately understanding source code poses significant chal-lenges, as source ...