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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems05:47

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

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This article describes RUGGED (Retrieval Under Graph-Guided Explainable disease Distinction), which integrates Large Language Model (LLM) inference with Retrieval-Augmented Generation (RAG). It draws evidence from expert-curated biomedical knowledge bases and peer-reviewed biomedical publications to synthesize new knowledge from up-to-date information, identify explainable and actionable predictions, and pinpoint promising directions for hypothesis-driven...
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A computational protocol, CaseOLAP LIFT, and a use case are presented for investigating mitochondrial proteins and their associations with cardiovascular disease as described in biomedical reports. This protocol can be easily adapted to study user-selected cellular components and...
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In this protocol, foundation large language model response quality is improved via augmentation with peer-reviewed, domain-specific scientific articles through a vector embedding mechanism. Additionally, code is provided to aid in performance comparison across large language...
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We present a protocol to explore the relationship between spatial language production, spatial memory, and object knowledge. The procedure allows experimental manipulation of, and control over, conditions of object knowledge, language at instruction, and physical location, thus teasing apart cognitive and linguistic models describing interactions between these...
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Existing algorithms generate one solution for a biomarker detection dataset. This protocol demonstrates the existence of multiple similarly effective solutions and presents a user-friendly software to help biomedical researchers investigate their datasets for the proposed challenge. Computer scientists may also provide this feature in their biomarker detection...
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Retrieval01:12

Retrieval

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Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
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Updated: Jan 20, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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生物医学ベンチマークにおける大規模言語モデルのパフォーマンスを向上させる連合知識検索

Janet Joy1, Andrew I Su1

  • 1Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, CA, USA.

GigaScience
|January 19, 2026
PubMed
まとめ
この要約は機械生成です。

BioThings Explorer（BTE-RAG）を使用した検索拡張生成は、生物医学研究における大規模言語モデル（LLM）の精度を向上させます。このフレームワークは、創薬およびトランスレーショナルサイエンスにおける事実の正確性とメカニズム探索を改善します。

キーワード:
大規模言語モデル検索拡張生成生物医学知識グラフ創薬

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関連する実験動画

Last Updated: Jan 20, 2026

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

  • 生物医学研究
  • 人工知能
  • 知識表現

背景:

  • 大規模言語モデル（LLM）は、生物医学研究のための高度な自然言語処理を提供します。
  • LLMは、暗黙のデータへの依存により、事実の不正確さ（幻覚）を生成する可能性があります。
  • これらの不正確さは、重要な生物医学的アプリケーションにおいてリスクをもたらします。

研究 の 目的:

  • 生物医学研究におけるLLMの精度を向上させるフレームワークを開発すること。
  • 明示的なメカニズム的証拠をLLMの推論に統合すること。
  • LLM出力における事実の正確性を向上させ、幻覚を減らすこと。

主な方法:

  • 検索拡張生成フレームワークであるBTE-RAGを開発しました。
  • LLMの推論とBioThings Explorerからの明示的な証拠（APIフェデレーション）を統合しました。
  • 3つのカスタムベンチマークデータセット（遺伝子メカニズム、代謝物効果、薬物-生物学的プロセス）で、LLMのみの方法と比較してBTE-RAGを評価しました。

主要な成果:

  • BTE-RAGは、遺伝子中心のタスク（例：GPT-4oの精度が69.8％から78.6％に向上）で精度を大幅に向上させました。
  • 代謝物効果に対する応答品質が向上しました（例：GPT-4o miniのコサイン類似度が高い応答が82％増加しました）。
  • 薬物と生物学的プロセスの関係に関する回答の一致が改善され、遺伝子疾患関連ベンチマークで代替モデルを上回りました。

結論:

  • BTE-RAGによる連合知識検索は、LLMの透明な精度向上を提供します。
  • BTE-RAGは、生物医学研究におけるメカニズム探索のための実用的なツールです。
  • このフレームワークは、LLMの信頼性を向上させることにより、トランスレーショナル生物医学研究をサポートします。