DeepEvidence: Empowering Biomedical Discovery with Deep Knowledge Graph Research

• URL: http://arxiv.org/abs/2601.11560v1
• Date: Tue, 23 Dec 2025 14:34:38 GMT
• Title: DeepEvidence: Empowering Biomedical Discovery with Deep Knowledge Graph Research
• Authors: Zifeng Wang, Zheng Chen, Ziwei Yang, Xuan Wang, Qiao Jin, Yifan Peng, Zhiyong Lu, Jimeng Sun,
• Abstract summary: We introduce DeepEvidence, an AI-agent framework designed to perform Deep Research across various biomedical knowledge graphs (KGs)<n>Unlike generic Deep Research systems that rely primarily on internet-scale text, DeepEvidence incorporates specialized knowledge-graph tooling and coordinated exploration strategies.<n>DeepEvidence demonstrates substantial gains in systematic exploration and evidence synthesis across four key stages of the biomedical discovery lifecycle.
• Score: 33.51246292480848
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Biomedical knowledge graphs (KGs) encode vast, heterogeneous information spanning literature, genes, pathways, drugs, diseases, and clinical trials, but leveraging them collectively for scientific discovery remains difficult. Their structural differences, continual evolution, and limited cross-resource alignment require substantial manual integration, limiting the depth and scale of knowledge exploration. We introduce DeepEvidence, an AI-agent framework designed to perform Deep Research across various heterogeneous biomedical KGs. Unlike generic Deep Research systems that rely primarily on internet-scale text, DeepEvidence incorporates specialized knowledge-graph tooling and coordinated exploration strategies to systematically bridge heterogeneous resources. At its core is an orchestrator that directs two complementary agents: Breadth-First ReSearch (BFRS) for broad, multi-graph entity search, and Depth-First ReSearch (DFRS) for multi-hop, evidence-focused reasoning. An internal, incrementally built evidence graph provides a structured record of retrieved entities, relations, and supporting evidence. To operate at scale, DeepEvidence includes unified interfaces for querying diverse biomedical APIs and an execution sandbox that enables programmatic data retrieval, extraction, and analysis. Across established deep-reasoning benchmarks and four key stages of the biomedical discovery lifecycle: drug discovery, pre-clinical experimentation, clinical trial development, and evidence-based medicine, DeepEvidence demonstrates substantial gains in systematic exploration and evidence synthesis. These results highlight the potential of knowledge-graph-driven Deep Research to accelerate biomedical discovery.

Related papers

• End-to-End Agentic RAG System Training for Traceable Diagnostic Reasoning [52.12425911708585]
  Deep-DxSearch is an agentic RAG system trained end-to-end with reinforcement learning (RL)<n>In Deep-DxSearch, we first construct a large-scale medical retrieval corpus comprising patient records and reliable medical knowledge sources.<n> Experiments demonstrate that our end-to-end RL training framework consistently outperforms prompt-engineering and training-free RAG approaches.
  arXiv  Detail & Related papers  (2025-08-21T17:42:47Z)
• MedResearcher-R1: Expert-Level Medical Deep Researcher via A Knowledge-Informed Trajectory Synthesis Framework [24.399778346443757]
  General-purpose deep research agents struggle with medical domain challenges, as evidenced by leading proprietary systems.<n>We present a medical deep research agent that addresses these challenges through two core innovations.<n>Our approach generates 2100+ diverse trajectories across 12 medical specialties, each averaging 4.2 tool interactions.
  arXiv  Detail & Related papers  (2025-08-20T17:51:20Z)
• A Retrieval-Augmented Knowledge Mining Method with Deep Thinking LLMs for Biomedical Research and Clinical Support [4.663954774358363]
  We introduce Integrated and Progressive Retrieval-Augmented Reasoning (IP-RAR) to enhance retrieval accuracy and knowledge reasoning.<n>IP-RAR maximizes information recall through Integrated Reasoning-based Retrieval and refines knowledge via Progressive Reasoning-based Generation.<n>This framework helps doctors efficiently integrate treatment evidence for personalized medication plans.
  arXiv  Detail & Related papers  (2025-03-29T09:56:42Z)
• Causal Representation Learning from Multimodal Biomedical Observations [57.00712157758845]
  We develop flexible identification conditions for multimodal data and principled methods to facilitate the understanding of biomedical datasets.<n>Key theoretical contribution is the structural sparsity of causal connections between modalities.<n>Results on a real-world human phenotype dataset are consistent with established biomedical research.
  arXiv  Detail & Related papers  (2024-11-10T16:40:27Z)
• Diversifying Knowledge Enhancement of Biomedical Language Models using Adapter Modules and Knowledge Graphs [54.223394825528665]
  We develop an approach that uses lightweight adapter modules to inject structured biomedical knowledge into pre-trained language models. We use two large KGs, the biomedical knowledge system UMLS and the novel biochemical OntoChem, with two prominent biomedical PLMs, PubMedBERT and BioLinkBERT. We show that our methodology leads to performance improvements in several instances while keeping requirements in computing power low.
  arXiv  Detail & Related papers  (2023-12-21T14:26:57Z)
• Data-Driven Information Extraction and Enrichment of Molecular Profiling Data for Cancer Cell Lines [1.1999555634662633]
  This work presents the design, implementation and application of a novel data extraction and exploration system. We introduce a new public data exploration portal, which enables automatic linking of genomic copy number variants plots with ranked, related entities. Our system is publicly available on the web at https://cancercelllines.org.
  arXiv  Detail & Related papers  (2023-07-03T11:15:42Z)
• A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises [59.4999994297993]
  This comprehensive review aims to provide an overview of the current state of Healthcare Knowledge Graphs (HKGs)<n>We thoroughly analyzed existing literature on HKGs, covering their construction methodologies, utilization techniques, and applications.<n>The review highlights the potential of HKGs to significantly impact biomedical research and clinical practice.
  arXiv  Detail & Related papers  (2023-06-07T21:51:56Z)
• BERT Based Clinical Knowledge Extraction for Biomedical Knowledge Graph Construction and Analysis [0.4893345190925178]
  We propose an end-to-end approach for knowledge extraction and analysis from biomedical clinical notes. The proposed framework can successfully extract relevant structured information with high accuracy.
  arXiv  Detail & Related papers  (2023-04-21T14:45:33Z)
• Towards Unified AI Drug Discovery with Multiple Knowledge Modalities [5.232382666884214]
  We propose KEDD, a unified, end-to-end, and multimodal deep learning framework. It optimally incorporates both structured and unstructured knowledge for vast AI drug discovery tasks. Our framework achieves a deeper understanding of molecule entities, brings significant improvements over state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-04-17T13:15:16Z)
• HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting [33.1455954220194]
  HiPrompt is a supervision-efficient knowledge fusion framework. It elicits the few-shot reasoning ability of large language models through hierarchy-oriented prompts. Empirical results on the collected KG-Hi-BKF benchmark datasets demonstrate the effectiveness of HiPrompt.
  arXiv  Detail & Related papers  (2023-04-12T16:54:26Z)
• EBOCA: Evidences for BiOmedical Concepts Association Ontology [55.41644538483948]
  This paper proposes EBOCA, an ontology that describes (i) biomedical domain concepts and associations between them, and (ii) evidences supporting these associations. Test data coming from a subset of DISNET and automatic association extractions from texts has been transformed to create a Knowledge Graph that can be used in real scenarios.
  arXiv  Detail & Related papers  (2022-08-01T18:47:03Z)
• Discovering Drug-Target Interaction Knowledge from Biomedical Literature [107.98712673387031]
  The Interaction between Drugs and Targets (DTI) in human body plays a crucial role in biomedical science and applications. As millions of papers come out every year in the biomedical domain, automatically discovering DTI knowledge from literature becomes an urgent demand in the industry. We explore the first end-to-end solution for this task by using generative approaches. We regard the DTI triplets as a sequence and use a Transformer-based model to directly generate them without using the detailed annotations of entities and relations.
  arXiv  Detail & Related papers  (2021-09-27T17:00:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.