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Connect fragmented evidence

Map drug-disease relationships with knowledge graphs

R&D Graphs Pilot Indirect

Knowledge graphs connect scientific, clinical, and reference data so researchers can explore relationships that are difficult to see across separate sources.

Abstract scientific data signals representing connected evidence
(01) The opportunity

Why this use case matters.

Siloed data makes it difficult to connect evidence across diseases, mechanisms, compounds, and outcomes.

Faster evidence synthesis, better reuse of institutional knowledge, and more focused hypothesis generation.

Primary user
Translational Research Scientist
Business process
Knowledge discovery and hypothesis generation
Primary technology
Knowledge Graphs
Human responsibility
Curate entities, assess evidence quality, and validate scientific interpretations
(02) How it works

From data to a human decision.

The technology supports a defined workflow. It does not replace the accountable professional.

01

Inputs and data

Drug databases, disease ontologies, literature, and curated internal evidence

02

AI capability

Resolves entities and relationships into a connected, searchable evidence model

03

Output and action

Relationship maps, traceable evidence paths, and candidate hypotheses

(03) Use case evaluation

Value versus effort.

This indicative view balances potential business value with the effort required to implement and operate the use case.

Selected use case Other use cases
(04) Responsible implementation

Risks and controls.

The value depends on a workflow that makes risk visible and preserves human accountability.

Risk areas

  • Poor source-data quality
  • Misleading inferred relationships
  • Complexity that obscures provenance

Practical controls

  • Expert curation
  • Visible source provenance
  • Regular ontology and data-quality review
Evaluate it in your context

Is this use case worth pursuing for your organization?

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