Biomedical Knowledge Graph: 1 Powerful Insight

Why Biomedical Knowledge Graphs Are Revolutionizing Drug Findy

A biomedical knowledge graph connects biological entities like genes, proteins, diseases, and drugs to uncover hidden patterns across vast datasets. By integrating disparate data sources—from EHRs and genomics to literature and clinical trials—these graphs provide a unified structure for research. This enables AI-powered findy of novel drug targets, accelerates research timelines through automated reasoning, and supports precision medicine with patient-specific treatment recommendations.

The pharmaceutical industry faces a significant data challenge, with clinical datasets siloed across institutions and traditional databases unable to capture complex biological relationships. Biomedical knowledge graphs address this by creating a unified, queryable framework of interconnected health data.

This technology transforms drug findy, disease understanding, and patient care. Researchers can trace pathways from genetic variants to disease phenotypes and potential therapies, leading to faster hypothesis generation, more targeted clinical trials, and improved success rates in drug development.

I’m Maria Chatzou Dunford, CEO and Co-founder of Lifebit. For over 15 years, I’ve focused on developing computational tools that integrate genomic and clinical data for precision medicine. My work involves creating biomedical knowledge graph platforms that enable secure, federated analysis across diverse healthcare datasets, ensuring patient privacy and regulatory compliance.

What is a Biomedical Knowledge Graph? The Blueprint of Biological Data

Biomedical researchers face a daily challenge: valuable data from genomics databases, patient records, and scientific literature is fragmented and difficult to connect. A biomedical knowledge graph acts as a master key, revealing how these scattered pieces fit together.

Unlike traditional databases that use rigid tables, a biomedical knowledge graph creates a dynamic network that mirrors the intricate web of life. It doesn’t just store data; it maps the meaningful relationships between biological concepts.

The graph’s power comes from integrating diverse data sources. It acts as a universal translator for public databases like NCBI’s suite (PubMed, ClinVar), protein repositories (UniProt), drug databases (DrugBank, ChEMBL), and disease catalogues (OMIM). It also ingests unstructured scientific literature, proprietary clinical trial data, and real-world evidence from Electronic Health Records (EHRs). Each source provides a unique layer of evidence: genomics data offers the biological blueprint, literature provides hypotheses and validated findings, and EHRs offer insights into how diseases manifest and respond to treatment in large populations. To ensure consistency, the graph relies on standardized ontologies. These are formal vocabularies that define and structure concepts and their relationships. For example, the Gene Ontology (GO) classifies gene functions, MONDO standardizes disease names to resolve ambiguities (e.g., distinguishing Type 1 from Type 2 diabetes), and SNOMED CT provides a comprehensive set of clinical terms. This semantic framework is what allows the graph to understand that a “myocardial infarction” in an EHR is the same as a “heart attack” in a research paper. For real-world examples, see this curated list of biomedical knowledge graphs.

Core Components and Domains

Every biomedical knowledge graph is built on two fundamental components:

• Nodes and entities: These represent individual concepts like genes (BRCA1), proteins (insulin), diseases (diabetes), drugs (aspirin), and phenotypes (high blood pressure).
• Edges and relationships: These describe the connections, such as a gene that “codes for” a protein, a drug that “treats” a disease, or a protein that “inhibits” another.

This structure allows us to map entire biological domains, from genomics and proteomics to disease networks. A researcher could trace a complex path: a specific genetic variant (node) is associated with the gene CFTR (node), which codes for a protein (node) whose dysfunction causes the disease Cystic Fibrosis (node), which is treated by the drug Ivacaftor (node). This reveals how different conditions relate to each other and to potential treatments.

Facilitating Knowledge Management

Biomedical knowledge graphs transform how we use biological information. Semantic search enables complex queries that are impossible in traditional databases, such as “Find all drugs that target proteins involved in inflammatory pathways and have been tested in Phase II clinical trials for autoimmune diseases.” Automated reasoning is a key capability, allowing the graph to infer new, implicit connections from existing explicit data. For instance, if the graph knows that Drug A inhibits Protein B, and Protein B’s activity is a known cause of Disease C, the system can generate a testable hypothesis that Drug A may be a potential treatment for Disease C. This process of inference enriches data interpretation by placing new findings within the context of all existing knowledge. By connecting disparate information, these graphs break down the data silos that have long hindered medical research, creating a single, navigable framework where genomic, clinical, and literature data converge.

From Raw Data to Actionable Insights: How BKGs Are Constructed

Building a biomedical knowledge graph is a sophisticated process of assembling information from countless sources, much of which is unstructured. The process begins with data ingestion, gathering heterogeneous data from both structured databases (like ChEMBL or ClinVar) and unstructured text like research papers, clinical notes, and patents.

Natural Language Processing (NLP) is essential for extracting structured facts from human language. Key NLP tasks include:

• Named Entity Recognition (NER): This identifies and categorizes important entities like genes (“BRCA1”), diseases (“non-small cell lung cancer”), and drugs (“Metformin”) within the text. This is challenging due to ambiguity—for example, the symbol “p53” can refer to the gene, the protein, or a related pathway.
• Relation extraction: This determines how entities are connected, such as identifying a directional “inhibits” relationship between a drug and a protein or a non-directional “is associated with” link between a gene and a disease. This often requires sophisticated models to understand sentence structure and context.

Once entities and relations are extracted, data harmonisation becomes critical. This involves resolving inconsistencies, such as different names for the same concept or different units of measurement. A core part of this is entity linking (or normalization), which ensures that mentions like “BRCA1,” “Breast cancer susceptibility gene 1,” and an accession number like “P38398” are all correctly mapped to the same unified node in the graph. This creates a clean, integrated dataset.

Graph Architecture and Technology

Under the hood, BKGs are typically built on one of two main data models. The Resource Description Framework (RDF) model, a W3C standard, represents data as a series of three-part statements (subject-predicate-object), known as triples. This model is excellent for data integration and semantic interoperability across different systems. The other common model is the Labeled Property Graph (LPG), used by graph databases like Neo4j. In an LPG, both nodes and edges can have properties (e.g., a ‘drug’ node can have a ‘brandname’ property, and a ‘treats’ edge can have a ‘confidencescore’ property). LPGs are often more intuitive and can be more performant for certain types of analytical queries, like pathfinding.

Ensuring Quality and Scalability

Trust is paramount, making data validation and curation fundamental. Every node and edge should ideally be traceable back to its source evidence, a concept known as provenance. This allows researchers to verify claims and assess the strength of the evidence. The curation process often involves a human-in-the-loop system, where automated algorithms flag uncertain or conflicting information for review by domain experts. The goal is to create a graph that is both accurate and comprehensive.

Scalability is another major challenge, as biomedical data grows exponentially. The graph’s infrastructure—both software and hardware—must be designed to handle billions of nodes and edges without performance degradation. This requires not only powerful graph databases but also ongoing maintenance and optimization to ensure the graph remains a living, responsive resource.

Powering Findy: Key Tasks and Applications of BKGs

The true value of a biomedical knowledge graph is demonstrated through its real-world applications in finding new treatments, understanding diseases, and improving patient care. By revealing previously invisible patterns, these graphs accelerate the pace of findy.

The translational impact is significant, bridging the gap between laboratory findings and clinical applications to make precision medicine a reality. For an example of a large-scale resource, you can read about the PrimeKG knowledge graph, which is accelerating drug findy efforts.

Drug Findy and Repurposing with a biomedical knowledge graph

Drug development is a long, expensive, and high-risk process. A biomedical knowledge graph fundamentally changes this paradigm.

• Target identification and validation: Researchers can trace disease pathways from genetic variations to symptoms, pinpointing the most effective points for intervention. A BKG can validate a potential target by aggregating multiple lines of evidence—for example, showing that a gene is mutated in patients, its protein product is overexpressed in diseased tissue, and inhibiting it has a therapeutic effect in animal models.
• Drug-target interaction: The graph provides a comprehensive view of how a drug’s interaction with its primary target ripples through interconnected biological pathways, helping to understand its mechanism of action.
• Side effect prediction: By analyzing a drug’s molecular targets and their associated pathways, the system can anticipate adverse effects. For example, if a new drug for arthritis is found to interact with a protein that is structurally similar to one crucial for cardiac function, the graph can flag a potential risk of cardiotoxicity before it appears in clinical trials.

Drug repurposing is one of the most powerful applications. By identifying unexpected connections between an existing drug’s mechanism and a different disease pathway, BKGs can find new uses for approved medications. A classic example is sildenafil, which was developed for angina but repurposed for erectile dysfunction. A BKG could have expedited this by connecting sildenafil’s mechanism (inhibiting the PDE5 enzyme) to biological pathways known to be involved in vasodilation relevant to the second condition. This approach leverages existing safety data to dramatically reduce development time and cost. Modern biomedical knowledge graph platforms can identify 1,200 candidate drugs or more for potential repurposing, offering a wealth of new therapeutic possibilities.

Precision Medicine and Patient Stratification

Biomedical knowledge graphs make the promise of precision medicine achievable by managing the complexity of individual health journeys.

• Personalized treatment: By integrating a patient’s genetic profile, medical history, and lifestyle factors, the graph can generate treatment recommendations custom to their unique biological context.
• Disease subtyping: The graph can identify distinct disease subtypes that are not apparent from symptoms alone. For example, in oncology, a BKG can integrate genomic data to differentiate breast cancer subtypes (e.g., Luminal A, HER2-positive, Triple-Negative) based on their molecular signatures, guiding clinicians to the most effective targeted therapies like tamoxifen or Herceptin.
• Biomarker findy: Researchers can quickly find measurable indicators (biomarkers) that help diagnose diseases earlier, monitor treatment efficacy, and predict patient responses.
• Patient similarity networks: Clinicians can find other patients with similar molecular and clinical profiles, allowing them to learn from past treatment outcomes to inform current care decisions.

Advancing Clinical Trial Design

BKGs can also revolutionize how clinical trials are designed and executed. By analyzing integrated data, researchers can perform more precise patient cohort selection, identifying individuals with the specific genetic markers or disease characteristics most likely to respond to a new therapy. This increases the chances of a successful trial. Furthermore, graphs can help identify geographic hotspots of eligible patient populations to optimize trial site selection and even predict potential trial failures by flagging safety risks or low-efficacy patient subgroups early in the process.

Overcoming Problems: Challenges and the Future of Biomedical Knowledge Graphs

While incredibly powerful, building and maintaining a biomedical knowledge graph presents several significant challenges. Key issues include overcoming data silos, where information is trapped in proprietary or incompatible systems. Managing data heterogeneity is another hurdle; for instance, integrating a gene expression dataset using Ensembl IDs with clinical data using ICD-10 codes requires complex and robust mapping. Graphs must also address data incompleteness and accuracy and noise, as much of the source data can be contradictory or contain errors. Furthermore, graphs must account for temporal dynamics—scientific knowledge evolves, and a relationship considered true today may be disproven tomorrow. The graph must be versioned and updatable. Finally, as data volumes explode, graphs face immense scalability challenges, and their quality must be assessed with robust evaluation metrics to ensure trust and reliability.

The Future of the biomedical knowledge graph: Emerging Trends

Despite these problems, the future of biomedical knowledge graph technology is bright, with several emerging trends ready to overcome current limitations.

• Large Language Models (LLMs) are revolutionizing knowledge extraction. However, their real power lies in synergy with BKGs. Using a technique called Retrieval-Augmented Generation (RAG), an LLM can query the BKG to retrieve factual, structured data to ground its responses. This prevents factual errors or “hallucinations” and combines the LLM’s natural language fluency with the BKG’s verifiable knowledge.
• Federated learning, an approach we champion at Lifebit, directly solves the data silo and privacy problem. Instead of centralizing sensitive patient data, analytical models are sent securely to the data’s location. The models are trained locally, and only the resulting anonymized parameters—not the raw data—are returned to a central point. This allows for the collaborative construction of a global biomedical knowledge graph while adhering to strict privacy regulations like GDPR and HIPAA.
• Multi-modal data integration is creating a more holistic view of health by incorporating not just text and genomics, but also medical images (like MRIs or pathology slides), wearable sensor data, and even social determinants of health into the graph structure.
• Automated graph construction and real-time updates are reducing the manual effort required to build and maintain these networks. This is changing graphs from static snapshots into living resources that reflect the latest scientific findings and clinical guidelines as they are published.
• Explainable AI (XAI) is becoming critical for building trust. When a graph suggests a new drug-disease link, XAI provides the reasoning and evidence behind it. For example, instead of a black-box prediction, it would present the evidence trail: “Drug X is recommended for Disease Y because it inhibits Protein P [Source: DrugBank], which is a key component of a pathway that is dysregulated in Disease Y [Source: Reactome], and genetic variants in Protein P are associated with higher risk for Disease Y [Source: ClinVar].” This transparency is essential for adoption by researchers and clinicians.

Frequently Asked Questions about Biomedical Knowledge Graphs

Here are answers to some of the most common questions about this transformative technology.

How do knowledge graphs differ from traditional databases?

The key difference lies in structure and flexibility. Traditional databases use rigid tables (like spreadsheets), requiring complex queries to find connections. A biomedical knowledge graph uses a flexible network structure of nodes (entities) and edges (relationships). This mirrors real-world biology and is easily updated. Most importantly, graphs understand semantic relationships (e.g., “treats,” “inhibits”), which enables powerful inference capabilities to uncover new insights that traditional databases cannot.

What role does AI play in biomedical knowledge graphs?

AI is essential for both building and utilizing a biomedical knowledge graph. Its roles include:

• Automated extraction: AI, especially Natural Language Processing, reads scientific literature to automatically extract entities and relationships, populating the graph at a superhuman scale.
• Link prediction: AI algorithms analyze existing patterns to predict new, undocumented relationships, generating novel hypotheses.
• Pattern recognition: Specialized AI like graph neural networks can identify complex patterns across the network to predict disease associations or patient responses.

In short, AI transforms the graph from a data repository into an active partner for knowledge findy.

Can BKGs predict new uses for existing drugs?

Yes, this is one of their most impactful applications. Drug repurposing with a biomedical knowledge graph is highly effective. By mapping a drug’s known mechanisms of action and targets, the graph can identify novel drug-disease links. For example, it might find that a heart medication targets a protein also involved in an inflammatory disease. This approach significantly reduces findy costs and timelines because the repurposed drugs have already passed safety trials. It’s a proven method for finding new treatments hiding in plain sight.

Conclusion

Biomedical knowledge graphs represent a fundamental shift in medical research, moving us from isolated data points to a connected, holistic view of human biology. They are changing biopharma by accelerating drug development, improving side effect prediction, and open uping thousands of drug repurposing opportunities.

By bridging research and clinical practice, these graphs close the gap between laboratory findings and patient care. The power of connected data is making precision medicine a reality, linking genomic information, clinical outcomes, and treatment responses to create insights far greater than the sum of their parts.

At Lifebit, we are dedicated to using this power. Our federated platform, featuring a Trusted Research Environment and Real-time Evidence & Analytics Layer, addresses the critical challenge of accessing sensitive health data securely. By bringing analysis to the data, our federated approach allows researchers to collaborate on global datasets without compromising patient privacy or regulatory compliance. This enables the construction of more comprehensive and powerful biomedical knowledge graphs.

The future of medicine is personalized, proactive, and powered by connected data. We are no longer wondering if this future is possible—we are building it.

Ready to see how connected data can transform your research? Explore how to leverage federated data for your research and find how our platform can open up new insights while keeping your data secure.