ArticleGigaScience2025
Knowledge graph-based thought: a knowledge graph-enhanced LLM framework for pan-cancer question answering.
Article in GigaScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Advancing Evidence-Based Medicine for Population, Intervention, Comparison, and Outcome Element Recognition and Extraction in Medical Literature: Large Language Model Approach.Journal of medical Internet research · 2026Article
- Review
- iS2C2: a cointelligent platform for mechanistic discovery of disease cellular crosstalk.Signal transduction and targeted therapy · 2026Article
- Development and comparative evaluation of knowledge graph-enhanced large language models for domain-specific question answering in nursing.BMC nursing · 2026Article
- AI-powered TargetMap: Enabling system-level target discovery through full-path reasoning on a unified knowledge graph.iScience · 2026Article
- Bridging data and discovery: a survey on knowledge graphs in AI for science.National science review · 2026Review
- textToKnowledgeGraph: generation of molecular interaction knowledge graphs using large language models for exploration in Cytoscape.Bioinformatics (Oxford, England) · 2026Article
- An agentic AI framework connecting language models to electronic health records and a biomedical knowledge graph for real-world evidence.Frontiers in artificial intelligence · 2026Article
- Desiderata for a biomedical knowledge network: opportunities, challenges and future directions.Bioinformatics advances · 2026Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- A framework for reconfigurable production line changeover task planning based on large language model.Scientific reports · 2025Article
- Improving Large Language Model Applications in the Medical and Nursing Domains With Retrieval-Augmented Generation: Scoping Review.Journal of medical Internet research · 2025Article
- Article
- A retrieval-augmented knowledge mining method with deep thinking LLMs for biomedical research and clinical support.GigaScience · 2025Article
- Knowledge graph-based thought: a knowledge graph-enhanced LLM framework for pan-cancer question answering.GigaScience · 2025Article
- A retrieval-augmented knowledge mining method with deep thinking LLMs for biomedical research and clinical support.GigaScience · 2025Article
- A natural language processing approach to support biomedical data harmonization: Leveraging large language models.PloS one · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundIn recent years, large language models (LLMs) have shown promise in various domains, notably in biomedical sciences. However, their real-world application is often limited by issues like erroneous outputs and hallucinatory responses.
resultsWe developed the knowledge graph-based thought (KGT) framework, an innovative solution that integrates LLMs with knowledge graphs (KGs) to improve their initial responses by utilizing verifiable information from KGs, thus significantly reducing factual errors in reasoning. The KGT framework demonstrates strong adaptability and performs well across various open-source LLMs. Notably, KGT can facilitate the discovery of new uses for existing drugs through potential drug-cancer associations and can assist in predicting resistance by analyzing relevant biomarkers and genetic mechanisms. To evaluate the knowledge graph question answering task within biomedicine, we utilize a pan-cancer knowledge graph to develop a pan-cancer question answering benchmark, named pan-cancer question answering.
conclusionsThe KGT framework substantially improves the accuracy and utility of LLMs in the biomedical field. This study serves as a proof of concept, demonstrating its exceptional performance in biomedical question answering.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.