Hakken: Future Biomedical Discovery Prediction

Hakken predicts future scientific relationships by combining language models and temporal knowledge graphs, enhancing biomedical discovery.

The paper introduces Hakken, an advanced system that predicts future scientific relationships using temporal knowledge graphs and language models for biomedical discoveries. Hakken demonstrated a high recall rate of 60.72% in predicting rare biomedical relations, though with lower precision, by leveraging both graph and text data. The system successfully supported two out of three wet-lab experiments targeting predicted relationships, showing its potential for generating actionable hypotheses. Hakken presents a system for predicting scientific relationships that have not yet been…

saved by

• Yoyo Yuan

related reading

• [2005.11401] Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasksarxiv.org
• Predicting Empirical AI Research Outcomes with Language Modelsarxiv.org
• Retrieval Augmented Generation: Streamlining the creation of intelligent natural language processing modelsai.facebook.com
• GraphRAG: New tool for complex data discovery now on GitHub - Microsoft Researchmicrosoft.com
• [2303.13948] Knowledge Graphs: Opportunities and Challengesarxiv.org
• Let Your Graph Do the Talking: Encoding Structured Data for LLMsarxiv.org
• 2025.acl-long.896.pdfaclanthology.org
• KGGen: Extracting Knowledge Graphs from Plain Text with Language Modelsarxiv.org
• title.txtsybrandt.com
• Language models of protein sequences at the scale of evolution enable accurate structure predictionbiorxiv.org
• Retrieval-Augmented Generation for Large Language Models: A Surveyarxiv.org
• Reasoning Models Reason Well, Until They Don'tarxiv.org