GraphTFactor is a new end-to-end framework that represents protein sequences as graphs to directly classify transcription factors. By encoding sequences into graph structures, it handles variable-length inputs and preserves relational information without length constraints. Experiments show that GraphTFactor outperforms state‑of‑the‑art methods on benchmark datasets and offers interpretable insights into sequence regions driving predictions, with an open-source package and web app for easy use.
arXiv:2607. 11374v1 Announce Type: new Abstract: Graph Foundation Models (GFMs) have emerged as a promising paradigm for learning transferable representations across diverse graph domains.
Graph Foundation Models (GFMs) have emerged as a promising paradigm for learning transferable representations across diverse graph domains. Recent advancements in GFMs have been largely dominated by two paradigms: Graph Neural Network and Large Language Model (LLM) based methods.
AtlasULP introduces a relation atlas that captures both global and local structural associations between node pairs, providing a comprehensive representation for link prediction across diverse graph domains. The framework employs a domain-guided prompting model that generates domain-aware prompt tokens from contextual structures, enabling adaptive in-context prediction. Experiments show that AtlasULP consistently outperforms state‑of‑the‑art methods on a variety of graph domains.
arXiv:2607. 01627v1 Announce Type: cross Abstract: Accurate protein-protein interaction (PPI) prediction is central to functional genomics, disease mechanism discovery, and drug development.
arXiv:2610.02517v1 Announce Type: new Abstract: Graph-structured data plays a pivotal role in modeling complex relationships. However, real-world graphs are often incomplete due to data collection an...
arXiv:2607. 13115v1 Announce Type: new Abstract: Small language models (SLMs) have shown promise for zero-shot molecular property prediction from SMILES strings, yet they often suffer from structural blindness because sequence representations under-specify key graph-topological cues.
arXiv:2609.37555v1 Announce Type: new Abstract: Drug discovery is a costly and high-risk process, where toxicity-related failures remain a major cause of attrition in both preclinical and clinical st...
arXiv:2606. 06364v1 Announce Type: new Abstract: Subgraph detection seeks to identify whether and where instances of query patterns occur within a larger graph.
arXiv:2606. 27413v1 Announce Type: cross Abstract: Understanding which genes control which traits in an organism remains one of the central challenges in biology.
arXiv:2607. 13120v1 Announce Type: cross Abstract: Inferring gene regulatory networks (GRNs) from single-cell transcriptomic data is crucial for biological discovery, yet existing approaches suffer from a fundamental misalignment with real-world needs.
arXiv:2509. 21489v4 Announce Type: replace Abstract: Graph foundation models face several fundamental challenges including transferability across diverse domains and data scarcity, which calls into question the very feasibility of creating such models.
arXiv:2605. 15511v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have become the dominant framework for inductive graph-level learning.