Causaly Knowledge Graph

外部

Causaly Knowledge Graph is a high-precision biomedical knowledge graph boasting 500 million reliable facts and 70 million directional relationships from trusted sources like MEDLINE, GWAS, patents, and clinical trials. Built and reviewed by PhD scientists, it delivers verifiable answers with inline citations, distinguishes causality from co-occurrence, and supports private data integration via Private Data Fabric. Pharma, biotech, and R&D teams use it to accelerate discoveries through visual exploration with Bio Graph, Scientific RAG, and automated workflows, saving thousands of research hours.

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カテゴリResearch & Data Analysis
Causaly Knowledge Graph

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説明

Causaly Knowledge Graph is a high-precision biomedical knowledge graph boasting 500 million reliable facts and 70 million directional relationships from trusted sources like MEDLINE, GWAS, patents, and clinical trials. Built and reviewed by PhD scientists, it delivers verifiable answers with inline citations, distinguishes causality from co-occurrence, and supports private data integration via Private Data Fabric. Pharma, biotech, and R&D teams use it to accelerate discoveries through visual exploration with Bio Graph, Scientific RAG, and automated workflows, saving thousands of research hours.

主な機能

  • High-precision biomedical KG with 500M facts and 70M directional relationships
  • Verifiable answers with inline citations
  • Private data integration and contextualization
  • Visual exploration via Bio Graph
  • Scientific RAG for hybrid graph + similarity retrieval

主な用途

  1. 1.Target prioritization and biomarker discovery
  2. 2.Competitive intelligence
  3. 3.Automating research workflows from discovery to clinical stages
  4. 4.Visual investigation of biomedical relationships

Causaly Knowledge Graph はあなたに合っていますか？

おすすめの用途

  • Pharma/biotech R&D teams needing speed and traceability in target ID, biomarkers
  • Life sciences researchers automating enterprise-scale workflows

向いていない用途

  • Smaller/non-enterprise teams facing setup complexity
  • Users requiring immediate ROI data or pricing transparency

際立った特徴

  • Custom scientific ontologies across hundreds of semantic categories
  • Continuous real-time update pipelines
  • Coverage of abstracts, full texts, preprints, GWAS, patents, ClinicalTrials.gov
  • Bio Graph API for programmatic access
  • Distinguishes causality vs co-occurrence

ユーザーフィードバックのハイライト

最も高く評価された点

  • Automates complex tasks, saving thousands of hours and 5x productivity vs PubMed
  • Traceable, hallucination-guarded insights with evidence matrices
  • Teva: target prioritization in 5 days vs 4 weeks
  • Praised for real-time updates by Ipsen, Teva users

よくある不満

  • Limited quantitative ROI metrics or independent benchmarks
  • New 2025 launch lacks long-term validation
  • No public pricing or accessibility details