Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Combination Therapies and Personalized Medicine

Combination Therapies and Personalized Medicine

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Hematopoietic Rejuvenation via Natural Senolytic NSPCC1 Delays Inflammatory Aging.

Biology·2026

Same author

Comprehensive analysis of interactions between brain aging and late-onset psychoses using heuristic mapping models.

Communications medicine·2026

Same author

Personalized-Context-Aware Age Gap: A New Multi-Omics Measurement Based on Age-Enhanced Model AOE-Net for Aging Acceleration and Chronic Disease Risk Prediction.

Aging cell·2026

Same author

European Bilberry Extract Ameliorates Dietary Advanced Glycation End Products-Induced Non-Alcoholic Steatohepatitis in Rats via Gut Microbiota and Its Metabolites.

Nutrients·2025

Same author

Molecular mechanism of resistance to lonafarnib conferred by mutations in the cysteine-rich region of respiratory syncytial virus fusion glycoprotein and discovery of a lonafarnib-derived antiviral PROTAC.

Journal of virology·2025

Same author

scTFBridge: a disentangled deep generative model informed by TF-motif binding for gene regulation inference in single-cell multi-omics.

Nature communications·2025

Search research articles

Related Experiment Video

Updated: Jan 17, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

A retrieval-augmented knowledge mining method with deep thinking LLMs for biomedical research and clinical support.

Yichun Feng^1,2,3, Jiawei Wang⁴, Ruikun He⁵

¹School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences, 100049 Beijing, China.

|September 19, 2025

Summary

This summary is machine-generated.

This study introduces a new method using large language models (LLMs) to build a biomedical knowledge graph and improve question answering. The Integrated and Progressive Retrieval-Augmented Reasoning (IP-RAR) method enhances information retrieval and reasoning accuracy.

Keywords:

deep thinking knowledge graph knowledge mining large language model retrieval-augmented generation

More Related Videos

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

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

Published on: June 13, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Related Experiment Videos

Last Updated: Jan 17, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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

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

Published on: June 13, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Area of Science:

Biomedical Informatics
Artificial Intelligence
Knowledge Representation

Background:

Biomedical knowledge integration and reasoning are crucial for scientific discovery.
Current knowledge graphs and LLMs face challenges in handling complex terminology, data heterogeneity, and rapid knowledge evolution.
Limitations exist in LLM retrieval and reasoning for uncovering cross-document associations.

Purpose of the Study:

To develop a pipeline for constructing a Biomedical Stratified Knowledge Graph (BioStrataKG) using LLMs.
To create the Biomedical Cross-Document Question Answering Dataset (BioCDQA) for evaluating knowledge retrieval and reasoning.
To introduce Integrated and Progressive Retrieval-Augmented Reasoning (IP-RAR) to enhance retrieval accuracy and reasoning.

Main Methods:

Utilized LLMs to construct BioStrataKG from large-scale biomedical articles.
Developed BioCDQA dataset for evaluating latent knowledge retrieval and multihop reasoning.
Implemented IP-RAR, featuring integrated reasoning-based retrieval and progressive reasoning-based generation with self-reflection.

Main Results:

IP-RAR significantly improved document retrieval F1 score by 20%.
Answer generation accuracy was enhanced by 25% compared to existing methods.
The approach demonstrated improved deep thinking and precise contextual understanding.

Conclusions:

IP-RAR aids clinicians in integrating treatment evidence for personalized medication plans.
Facilitates researchers in analyzing advancements and identifying research gaps.
Accelerates hypothesis generation in scientific discovery and decision-making.