Biomedical KGs

KG: Knowledge Graph

Biomedical knowledge graphs connect entities such as genes, diseases, drugs, pathways, and patient findings, often combining literature, databases, and clinical records. Researchers build and refine these graphs with information extraction, ontologies, provenance tracking, and temporal or patient-specific context, then use graph embeddings and neural networks to predict associations or rank candidates. Retrieval-augmented language models and graph-based agents use relevant subgraphs to answer biomedical questions, explain mechanisms, and support tasks such as diagnosis and drug discovery. A central challenge is ensuring that graphs are complete and reliable, and that models use retrieved evidence correctly rather than producing plausible but unsupported conclusions.

Latest papers

71 papers9 papers in the last four weeks, up 13% on the four weeks before. 0.1% of all new papers.

GIP-RAG: An Evidence-Grounded Retrieval-Augmented Framework for Interpretable Gene Interaction and Pathway Impact Analysis
Mar 19, 2026Fujian Jia, Jiwen Gu, Cheng Lu +5LLM Reasoning with Graphs Gene Regulatory Network Inference
Kanghua Juntai Biotech Co. Ltd., Room 1504, Building 7, Dongwang Jingyuan, No. 768 Jingwang Road, Kunshan, Suzhou, Jiangsu Province, China · Department of Radiation Oncology, Cancer Treatment Center, The Second Affiliated Hospital of Hainan Medical University, Haikou, China · Department of Nutrition, University of California, Davis, USA +1
Knowledge Graph Extraction from Biomedical Literature for Alkaptonuria Rare Disease
Mar 16, 2026Giang Pham, Rebecca Finetti, Caterina Graziani +12KG Construction Biomedical KGs
Department of Computer Science, University of Pisa, Pisa, Italy. · Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Siena, Italy · Department of Information Engineering and Mathematics, University of Siena, Siena, Italy +2
Open Biomedical Knowledge Graphs at Scale: Construction, Federation, and AI Agent Access with Samyama Graph Database
Mar 16, 2026Madhulatha Mandarapu, Sandeep KunkunuruBiomedical KGs KG Construction
VaidhyaMegha Private Limited, India
A Hybrid Knowledge-Grounded Framework for Safety and Traceability in Prescription Verification
Mar 11, 2026Yichi Zhu, Kan Ling, Xu Liu +3Knowledge Graphs Biomedical KGs
School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China
Pharmacology Knowledge Graphs: Do We Need Chemical Structure for Drug Repurposing?
Mar 2, 2026Youssef Abo-Dahab, Ruby Hernandez, Ismael Caleb Arechiga DuranBiomedical KGs KG Embedding
University of California, San Francisco, San Francisco, CA, USA. · Stanford University, Stanford, CA, USA.
From Literature to Hypotheses: An AI Co-Scientist System for Biomarker-Guided Drug Combination Hypothesis Generation
Feb 28, 2026Raneen Younis, Suvinava Basak, Lukas Chavez +1Scientific Hypothesis Generation Oncology
Peter L. Reichertz Institute for Medical Informatics (PLRI), Hannover Medical School · Lower Saxony Center for Artificial Intelligence and Causal Methods in Medicine (CAIMed) · Sanford Burnham Prebys (SBP) Medical Discovery Institute
GLaDiGAtor: Language-Model-Augmented Multi-Relation Graph Learning for Predicting Disease-Gene Associations
Feb 21, 2026Osman Onur Kuzucu, Tunca DoğanHeterogeneous GNNs Biomedical KGs
Biological Data Science Lab, Dept. of Computer Engineering, Hacettepe University, 06800, Ankara, Turkey. · Dept. of Bioinformatics, Graduate School of Health Sciences, Hacettepe University, 06800, Ankara, Turkey. · Dept. of Health Informatics, Institute of Informatics, Hacettepe University, 06800, Ankara, Turkey.
CUICurate: A GraphRAG-based Framework for Automated Clinical Concept Curation for NLP applications
Feb 20, 2026Victoria Blake, Jamie Novak, Mathew Miller +2Clinical NLP Graph Retrieval-Augmented Generation
Centre for Big Data Research in Health, University of New South Wales, Sydney, New South Wales, Australia · Eastern Heart Clinic, Prince of Wales Hospital, Randwick, NSW, Australia · Department of Cardiology, Prince of Wales Hospital, South Eastern Sydney Local Health District, Randwick, NSW, Australia +3
Exploring Drug Safety Through Knowledge Graphs: Protein Kinase Inhibitors as a Case Study
Feb 17, 2026David Jackson, Michael Gertz, Jürgen HesserClinical Risk Prediction Biomedical KGs
Institute of Informatics, University of Amsterdam, Amsterdam, Netherlands · Institute of Computer Science, Heidelberg University, Heidelberg, Germany · Data Analysis and Modeling in Medicine, Mannheim Institute for Intelligent Systems in Medicine (MIISM), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
BioPIE: A Biomedical Protocol Information Extraction Dataset for Experiment Understanding
Jan 8, 2026Haofei Hou, Shunyi Zhao, Fanxu Meng +3KG Construction Document Information Extraction
School of Advanced Manufacturing and Robotics, Peking University · School of Integrated Circuits, Peking University
MEDMKG: Benchmarking Medical Knowledge Exploitation with Multimodal Knowledge Graph
May 22, 2025Xiaochen Wang, Yuan Zhong, Lingwei Zhang +3Multimodal KGs Biomedical KGs
Pennsylvania State University, USA · Renmin Hospital of Wuhan University, China · Stony Brook University, USA

Mar 19, 2026J/K move · Enter open · S save

GIP-RAG: An Evidence-Grounded Retrieval-Augmented Framework for Interpretable Gene Interaction and Pathway Impact Analysis

Fujian Jia, Jiwen Gu, Cheng Lu, Dezhi Zhao+4

Kanghua Juntai Biotech Co. Ltd., Room 1504, Building 7, Dongwang Jingyuan, No. 768 Jingwang Road, Kunshan, Suzhou, Jiangsu Province, China · Department of Radiation Oncology, Cancer Treatment Center, The Second Affiliated Hospital of Hainan Medical University, Haikou, China · Department of Nutrition, University of California, Davis, USA+1

LLM Reasoning with Graphs Gene Regulatory Network Inference Biomedical KGs

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Understanding mechanistic relationships among genes and their impacts on biological pathways is essential for elucidating disease mechanisms and advancing precision medicine. Despite the availability of extensive molecular interaction and pathway data in public databases, integrating heterogeneous knowledge sources and enabling interpretable multi-step reasoning across biological networks remain challenging. We present GIP-RAG (Gene Interaction Prediction through Retrieval-Augmented Generation), a computational framework that combines biomedical knowledge graphs with large language models (LLMs) to infer and interpret gene interactions. The framework constructs a unified gene interaction knowledge graph by integrating curated data from KEGG, WikiPathways, SIGNOR, Pathway Commons, and PubChem. Given user-specified genes, a query-driven module retrieves relevant subgraphs, which are incorporated into structured prompts to guide LLM-based stepwise reasoning. This enables identification of direct and indirect regulatory relationships and generation of mechanistic explanations supported by biological evidence. Beyond pairwise interactions, GIP-RAG includes a pathway-level functional impact module that simulates propagation of gene perturbations through signaling networks and evaluates potential pathway state changes. Evaluation across diverse biological scenarios demonstrates that the framework generates consistent, interpretable, and evidence-supported insights into gene regulatory mechanisms. Overall, GIP-RAG provides a general and interpretable approach for integrating knowledge graphs with retrieval-augmented LLMs to support mechanistic reasoning in complex molecular systems.