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KG-TRACE is a neuro-symbolic framework that integrates a WHO mutation knowledge graph with a neural genomic model for antimicrobial resistance prediction, achieving high accuracy and introducing a Biological Grounding Ratio metric to ensure alignment with established biological knowledge.
BioManus is an MCP-native biomedical agent system that uses graph-scaffolded planning over structured biological capabilities instead of flat prompt-based tool retrieval, achieving better context efficiency and execution accuracy on biomedical benchmarks. The system introduces a BioinfoMCP Compiler to standardize heterogeneous bioinformatics tools and organizes them as a typed heterogeneous MCP graph for scalable reasoning.
GiG is a knowledge graph-modulated deep learning framework that integrates biological knowledge graphs as edges and patient-specific data as node features, outperforming SOTA by up to 49% in limited-sample clinical tasks.
This paper demonstrates that switching from Masked Language Modeling to Causal Language Modeling during encoder adaptation improves downstream performance on biomedical texts. The authors release ModernBERT-bio and ModernCamemBERT-bio as state-of-the-art biomedical encoders.
MIT released FINGERS-7B, a 7-billion-parameter multi-omics foundation model trained on data from 30,000 individuals to predict Alzheimer's risk years in advance. The model is accessible via the AD Workbench and is accompanied by a research paper on OpenReview.
This paper introduces NATD-GSSL, a framework evaluating the robustness of Graph Self-Supervised Learning on noisy, text-driven biomedical graphs. It demonstrates that certain GNN architectures and pretext tasks maintain performance despite real-world noise, offering practical guidance for unsupervised learning in imperfect datasets.
Researchers fine-tuned BioMistral-7B with QLoRA and GraphRAG to create a TB-care LLM for South Africa, showing improved contextual alignment over the base model.
LogosKG introduces a hardware-aligned framework for scalable, interpretable multi-hop retrieval on billion-edge knowledge graphs, integrating degree-aware partitioning and on-demand caching to boost efficiency without sacrificing fidelity.