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Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
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Related Experiment Video

Updated: Mar 13, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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Knowledge-Guided Explainable Recommendation Tool for Cancer Risk Prediction Models Using Retrieval-Augmented Large

Shumin Ren1,2, Xin Zheng1, Jing Zhao1

  • 1Institutes for Systems Genetics, West China Hospital of Sichuan University, Frontiers Science Center for Disease-related Molecular Network, Chengdu, Sichuan, 610041, China, 86 15995854635.

JMIR Medical Informatics
|March 11, 2026
PubMed
Summary
This summary is machine-generated.

A new system, CanRisk-RAG, enhances cancer risk prediction model discovery by integrating a large knowledge base and advanced AI. It offers more accurate and reliable recommendations than existing tools, improving precision prevention strategies.

Keywords:
LLMsRAGbiomedical information retrievalcancer risk prediction modelslarge language modelspersonalized medicineretrieval-augmented generation

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Area of Science:

  • Oncology
  • Biomedical Informatics
  • Computational Biology

Background:

  • Cancer risk prediction models are crucial for personalized prevention strategies.
  • Current limitations include fragmented resources and lack of structured discovery systems.

Purpose of the Study:

  • To develop a retrieval-augmented, knowledge-guided system for accurate cancer risk prediction model recommendations.

Main Methods:

  • Developed CanRisk-RAG with a knowledge base of over 800 models.
  • Integrated LLM-based semantic tag extraction, embedding vectorization, and multifactor ranking.
  • Evaluated performance against PubMed, ChatGPT-4o, ScholarAI, and Gemini 1.5 Flash.

Main Results:

  • CanRisk-RAG outperformed baseline applications in relevance and reliability.
  • Demonstrated high authenticity, data completeness, and consistency.
  • Provided accurate, structured recommendations, unlike incomplete or fabricated results from baselines.

Conclusions:

  • CanRisk-RAG offers a transparent, semantically enriched framework for cancer risk model discovery.
  • The system improves precision, reproducibility, and usability in model selection.
  • The framework shows potential for broader applications in precision medicine.