newsNature Machine IntelligenceTrust 88 · LabPublished 1mo agoLive · 1mo ago
Empowering biomedical evidence exploration and synthesis with deep knowledge graph research
Nature Machine Intelligence, Published online: 02 July 2026; doi:10.1038/s42256-026-01266-0 Wang et al. develop DeepEvidence, a biomedical deep research agent for exploring and synthesizing evidence across various knowledge sources to support drug discovery, clinical trials and evidence-based medicine.
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Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- LinkedLinked via unknownAutonomous Scientific Discovery via Iterative Meta-Reflection →
- LinkedLinked via unknownardhaecosystem/synapse →
- LinkedLinked via unknownBeyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) Benchmark →
- LinkedLinked via unknownScientific Explanations in Health Sciences: Causality, Trust, and Epistemic Adequacy →
- PossiblePossibly related (embedding) · 54%An Additive MLP-GNN Framework for Characterizing Chemical and Structural Contributions to Aqueous Solubility →
- PossiblePossibly related (embedding) · 49%Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations →
- PossiblePossibly related (embedding) · 46%URSA: Chemistry-Aware Benchmark for Utilitarian Retrosynthesis Assessment →
Covers
paperGraph-Native Reinforcement Learning Enables Traceable Scientific Hypothesis Generation through Conceptual RecombinationpaperAutonomous Scientific Discovery via Iterative Meta-Reflectionrepoardhaecosystem/synapsepaperBeyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) BenchmarkpaperScientific Explanations in Health Sciences: Causality, Trust, and Epistemic Adequacy
Covers (incoming)
paperAn Additive MLP-GNN Framework for Characterizing Chemical and Structural Contributions to Aqueous SolubilitypaperPredicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation RepresentationspaperURSA: Chemistry-Aware Benchmark for Utilitarian Retrosynthesis AssessmentpaperBiologically Informed Deep Neural Networks for Multi-Omic Integration, Pathway Activity Inference and Risk Stratification in CancerpaperCanopy: A Heterograph Foundation Model for Metabolic EngineeringpaperIdeas Have Genomes: Benchmarking Scientific Lineage Reasoning and Lineage-Grounded Idea GenerationpaperBenchmarking Resource-Efficient LLMs for Research Topic Ontology Generation in the Biomedical FieldpaperM$^3$-Gen: Interpretable Multimodal Generation of Gene Expression Profiles Using Clinical and Imaging DatapaperAgent-Guided Relational Concept Discovery: Toward Interpretable Surgical Margin Assessment
Related across the graph
repoardhaecosystem/synapsepaperBiologically Informed Deep Neural Networks for Multi-Omic Integration, Pathway Activity Inference and Risk Stratification in CancerpaperCanopy: A Heterograph Foundation Model for Metabolic EngineeringpaperM$^3$-Gen: Interpretable Multimodal Generation of Gene Expression Profiles Using Clinical and Imaging DatapaperBeyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) BenchmarkpaperBenchmarking Resource-Efficient LLMs for Research Topic Ontology Generation in the Biomedical FieldpaperIdeas Have Genomes: Benchmarking Scientific Lineage Reasoning and Lineage-Grounded Idea GenerationpaperAgent-Guided Relational Concept Discovery: Toward Interpretable Surgical Margin AssessmentpaperPredicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation RepresentationspaperAutonomous Scientific Discovery via Iterative Meta-ReflectionpaperURSA: Chemistry-Aware Benchmark for Utilitarian Retrosynthesis AssessmentpaperAn Additive MLP-GNN Framework for Characterizing Chemical and Structural Contributions to Aqueous SolubilitypaperScientific Explanations in Health Sciences: Causality, Trust, and Epistemic AdequacypaperGraph-Native Reinforcement Learning Enables Traceable Scientific Hypothesis Generation through Conceptual Recombination