Every time we search for something online, we’re interacting with a system that connects pieces of information behind the scenes. Search for a well-known scientist, and instead of just links, you’ll see related concepts, collaborators, and discoveries, all tied together in a meaningful way.

That same idea, connecting information in a meaningful way, is what underpins a knowledge graph.

In biomedical research, this becomes especially powerful. Instead of linking web pages, knowledge graphs connect genes, diseases, drugs, pathways, and clinical observations. The goal isn’t just organization, it’s understanding how these pieces relate to one another in a way that supports discovery.

That said, the way knowledge graphs are built is starting to matter just as much as the concept itself.

Many traditional approaches rely heavily on published literature. While that provides a useful starting point, it often captures associations at a surface level and can miss the depth that comes from experimental and multi modal datasets.

This is where a more data centric approach is beginning to take shape, one that integrates diverse datasets first and uses literature to add context, rather than the other way around.

What Are Knowledge Graphs and How Do They Work?

At a basic level, a knowledge graph organizes information as a connected network of data.

Key Components of a Knowledge Graph:

• Entities (Nodes): The fundamental units of a knowledge graph, representing concepts like genes, diseases, or molecules.
• Relationships (Edges): The connections between entities, such as "Gene X is associated with Disease Y" or "Protein A interacts with Protein B."
• Attributes: Additional metadata describing entities, such as a gene’s function or a drug’s mechanism of action.
• Ontology and Schema: The rules and structure that define how different types of entities and relationships are categorized and linked.

Why Traditional Knowledge Graphs Have Limitations

A large number of knowledge graphs in life sciences are built primarily from scientific literature. These systems extract relationships based on co-occurrence, how often genes, diseases, or drugs appear together in papers. While useful, this approach comes with trade-offs as literature tends to be:

• Biased toward well-studied areas
• Limited in quantitative depth
• Focused more on reported associations than experimentally validated relationships

As a result, many literature-based graphs capture relationships that are directionally unclear or lack the context needed for more complex biological questions. This doesn’t make them ineffective but it does highlight the need for approaches that can go deeper, especially as datasets continue to grow in scale and complexity.

A Shift Toward Data-Centric Knowledge Graphs

More recently, there’s been a shift toward building knowledge graphs that are grounded in curated, multi-modal datasets.

In this model, data from genomics, transcriptomics, proteomics, clinical studies, and molecular interactions forms the foundation. Literature is still used but more as a layer of supporting evidence rather than the primary source.

This change brings a few important advantages:

• Relationships are more likely to be evidence-backed
• Context from multiple data types is preserved
• The graph becomes more adaptable to new data

Polly KG follows this data-centric approach. It integrates multiple data modalities and aligns them in a way that reflects biological systems more closely, rather than relying solely on what’s been published.

How Polly KG Fits Into Research Workflows

One of the more practical differences with Polly KG is how it’s implemented.

Instead of being offered as a fixed product, it’s typically developed  in collaboration with research teams as a platform-as-a-service (PaaS) model.  This co- building approach allows the graph to reflect specific scientific questions, internal datasets, and existing workflows.

There are two main layers involved:

• A foundational knowledge layer built from curated public datasets
• A set of extensions that adapt the graph to specific use cases

This setup tends to make the system more usable in day-to-day research, especially when working across different data types or therapeutic areas.

Integrating Multi-Modal Data at Scale

Modern biomedical questions rarely rely on a single type of data. Understanding disease mechanisms, for instance, often requires combining different types of data like Genomic and transcriptomic data, Proteomic and metabolomic profiles, Clinical and phenotypic information, Literature-derived insights.

Polly KG supports integration across 30+ data modalities

This creates a more cohesive view of biological systems, making it easier to explore relationships that span different data types.

Applications in Biomedical Research

Knowledge graphs are already being used across several areas in life sciences:

1.Drug Discovery and Target Identification: By connecting genes, proteins, and pathways, knowledge graphs help identify potential therapeutic targets and understand their biological context.

2.Biomarker Discovery: Integrating multi-omics data allows researchers to uncover markers associated with disease progression or treatment response.

3.Personalized Medicine: Linking patient data with molecular insights supports more tailored treatment strategies.

4.Literature-Based Discovery (Enhanced with Data): Combining NLP with structured data enables more efficient extraction of insights from large volumes of research.

5.Safety and Efficacy Modeling: Knowledge graphs can connect pharmacokinetics with adverse events, enabling more detailed analysis of dose–response relationships and toxicity patterns.

Impact of PollyKG

A biotech company exploring cross-species disease biology was working with fragmented datasets across multiple modalities. Integrating and analyzing this data in a meaningful way was proving time-intensive.

Using PollyKG, they were able to bring together curated datasets, align them across species, and apply scoring frameworks tailored to their research goals.

Over a period of six months, this approach began to show tangible outcomes. The team was able to:

• Identify five high-confidence therapeutic targets
• Generate cross-species insights at scale
• Support findings with evidence-backed relationships across datasets

Read the full case study here.

Explore More

If you’re interested in how knowledge graphs are applied in real-world research:

• Target Discovery and Independent Orthogonal Validation for Alzheimer's Disease - Tau Pathology
• Target Discovery and Independent Orthogonal Validation for Small Cell Lung Carcinoma

To see how this approach could fit into your workflows, it may be worth seeing how a data-centric knowledge graph can be tailored to your research needs. Connect with us to build your own Polly Knowledge Graph.

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