In the rapidly advancing field of life sciences, the ability to synthesize vast amounts of information is no longer just an advantage, it is a necessity. For years, Knowledge Graphs (KGs) have served as the backbone for connecting disparate pieces of biological information. However, as our understanding of disease grows more complex, the methods we use to map that knowledge must also evolve.

Why Traditional Knowledge Graphs Have Limitations

A large number of knowledge graphs in life sciences are built primarily from scientific literature such as PubMed abstracts or full articles. 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 curated public datasets, multi-omics profiles(genomics, transcriptomics, proteomics), clinical and phenotypic data, molecular interactions,
and literature evidence into a unified, evidence-backed knowledge graph. Every relationship in
Polly KG is traceable, biologically grounded, and designed for hypothesis-driven exploration,
enabling a deeper understanding of complex biological systems.

How Polly KG Fits Into Research Workflows

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

Polly KG is not a one-size-fits-all fixed platform focused on broad data aggregation or literature-based associations. Instead, it is delivered as a Platform-as-a-Service (PaaS), a model intentionally developed and designed to evolve with each customer’s data ecosystem, scientific priorities, and analytical goals. This  co- building approach approach prioritizes customization, scalability, and iterative improvement, ensuring that each deployment reflects the specific biological and research context of the organization.

There are two main layers involved:

• Base-KG: A standardized foundation built from high-quality public data and literature context, enriched with natural language querying. It connects genes, diseases, drugs, pathways, and phenotypes to provide biologically meaningful relationships for target identification and validation.
• Accelerators: Customization tools that extend Base-KG to specific research needs by adding new entities, relationships, and scoring frameworks which enables scalable, domain-specific knowledge graphs aligned with proprietary data and research goals.

If you need an instant solution, traditional SaaS tools (e.g., Causaly, Qiagen Digital Insights) may be suitable. However, if the goal is to capture your unique biological context in a purpose-built knowledge graph, Polly KG’s data-centric approach delivers a more practical and flexible solution for everyday research across diverse data types and therapeutic areas.

Integrating Multi-Modal Data at Scale

Modern biomedical questions rarely rely on a single type of data. Understanding disease mechanisms 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 (View Full List of Supported Modalities and Source). This creates a more cohesive view of biological systems, making it easier to explore relationships that span different data types.

Real-World Impact

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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