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LinkedLife Data Inventory

Get access to 200+ pre-processed and ready-to-use datasets and ontologies in RDF format about genomics, proteomics, pharmacology and more to enrich your proprietary data, unlock new insights, and accelerate R&D

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Success story
Who is it for?
How does it work?
What to expect?
The underlying solution
Talk to LLDI
Related Resources

Graphwise’s LinkedLife Data Inventory (LLDI) empowers you to

  • Access an extensive data collection from sources like UniProt, ChEMBL, PubMed, and ClinicalTrials.gov, OpenTargets, DisGenet and many more
  • Cut data operations costs by automating data ingestion and updates
  • Expedite R&D and the discovery of new therapeutic targets
  • Bring confidence to your AI with domain knowledge and contextualized data
  • Enhance insights and innovation by identifying relationships in both structured and unstructured data
  • Improve regulatory compliance by linking and validating diverse data sources

“LinkedLife Data Inventory solution does what they need it to do. The willingness of the Graphwise team to adjust the tool based on needs was a critical point. We formed a relationship with them that made a difference – and our ability to handle large data sets, to find and rank choices is now so much better and faster!”


Anonymous
Researcher, Leading US biomedical and genomic research center

Who is Graphwise’s LinkedLife Data
Inventory for?

Pharma companies

Discover and repurpose a number of existing drugs to treat rare and newly identified diseases.

Biotech companies

Use target data of drug indications and build model datasets.

Research

Navigate efficiently a comprehensive data inventory of genes, proteins, compounds, diseases, and more.

50-70%

Lower Data Operation Costs

3-5x

higher adoption across researchers

How it works

1

Step 1:

Our established FAIRification process ensures the semantic harmonization of the data, normalizing property values to corresponding ontology / terminology instances specific for the biomedical domain.

2

Step 2:

For datasets serialized in RDF by their official publishers, we generate additional semantic mappings between certain concepts from referential datasets for genes, proteins, drugs, compounds, pathways, diseases, cell types and cell lines.

3

Step 3:

Whenever necessary for the delivery of a custom knowledge graph solution, we provide a definition of the mappings between the customer proprietary ontology and the incorporated public datasets from our data inventory.

4

Step 4:

Each single dataset in the data inventory is represented by a documented schema and a detailed description of each RDF serialization including classes, properties and semantic mappings.

What to expect?

Target Discovery – The Underlying Knowledge Graph Solution

AI-powered solution for intelligent target identification and selection helping companies to:

  • Accelerate drug discovery & clinical research by centralizing biomedical knowledge to discover new hypotheses and facilitate data-driven decision-making
  • Select the best targets by using customizable analytical methods on various data sources with transparent provenance
  • Reduce the cost and time for validation through intelligent ranking and prioritization of targets and hits
10x

Faster Drug Discovery

5x

Lower Cost & Time for Target Validation

Talk to LLDI – Powered Target Identification Graph

Knowledge Graphs hold immense potential for scientific discovery, but their complexity, massive scale, and steep technical requirements often make them inaccessible to researchers. Graphwise’s natural language query interface breaks down these barriers, empowering scientists to explore target identification and selection data as easily as asking a question.

This solution makes complex data accessible, visual, and actionable for every researcher.

Key Challenges

  • Complex and evolving schemas – The Knowledge Graphs structure can be hard to navigate and understand
  • Specialized skills required – Graph query languages are often unfamiliar to most researchers
  • Fragmented tools – Limited, inconsistent options for visualization and analysis
  • Overwhelming scale – Billions of connections make queries slow and results hard to digest
  • Difficult to interpret – Dense, interconnected results often need advanced summarization

How it helps

  • Accessible for all – Query in natural language, no coding
  • Faster insights – Get answers instantly, no need for specialists
  • Stronger ROI – Maximize value of your KG investment

Key benefits

  • Accelerate drug discovery and clinical research by 10x
  • Select the best targets
  • Reduce the cost and time for validation by 5x
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