Personalisierte Medizin

Stock, Günter; Sydow, Sabine

doi:10.1007/s00103-013-1820-4

Cited by 7 publications

(1 citation statement)

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“…The advancement of personalized medicine is predicated on the ability to tailor treatments to individual patient characteristics [45], particularly their genetic makeup. Individualized treatment is crucial for optimizing therapeutic efficacy and minimizing adverse effects.…”

Section: Resultsmentioning

confidence: 99%

Knowledge Graph-based Thought: a knowledge graph enhanced LLMs framework for pan-cancer question answering

Feng,

Zhou,

Zheng

et al. 2024

Preprint

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Background. In 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. Results. We 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 the Pan-cancer Question Answering (PcQA). Conclusions. The KGT framework substantially improves the accuracy and utility of LLMs in the biomedical field, demonstrating its exceptional performance in biomedical question answering.

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Section: Resultsmentioning

confidence: 99%