What Makes Knowledge Graphs Essential in Biomedical Research Today?

Every time we search for something online, we tap into a structured network of knowledge. Type in "Einstein," and in addition to a list of web pages mentioning his name, you also see contextual connections, such as the theory of relativity and even suggestions for related figures like Isaac Newton. This is powered by a knowledge graph, a system that links data points and captures relationships to provide meaningful insights.

The term "Knowledge Graph" was coined by Google in 2012 to describe the vast web of interconnected knowledge they had accumulated on various topics. Unlike traditional databases that store information in tables, a knowledge graph organizes data in a graph-based structure, where entities (e.g., people, places, diseases, genes) are represented as nodes and their relationships as edges. This approach helps human experts and machines visualize complex mechanistic relationships, thereby providing deeper context and enabling new insights.

Unlike structured repositories, which primarily store and retrieve well-defined data in organized formats, knowledge graphs establish connections between diverse datasets, allowing for dynamic exploration and hypothesis generation. Thus, while both structured repositories and knowledge graphs are sources of organized data, only knowledge graphs have the capability to generate new insights.

In addition to search engines, knowledge graphs are widely used in virtual assistants and chatbots like Siri and Alexa to understand and process natural language queries. Recommendation systems in platforms like Netflix, Spotify and Amazon leverage knowledge graphs to analyze user preferences and suggest relevant content and products. In finance, they assist in fraud detection and risk assessment by mapping complex transactional networks, while in cybersecurity, they help detect threats by analyzing relationships between attack patterns and vulnerabilities. In life sciences, knowledge graphs integrate massive amounts of biomedical data and aid in drug discovery, precision medicine and scientific hypothesis generation and discovery.

In this blog we will explore what knowledge graphs are, how they function, and the key benefits they offer for biopharma and biomedical research. We will also cover best practices for building and utilizing knowledge graphs effectively with examples of how Elucidata helped construct knowledge graphs and aided in scientific discovery.

What Are Knowledge Graphs and How Do They Work?

Knowledge graphs represent data as nodes (entities) and edges (relationships), structuring information into an interconnected network. In a biomedical context, a knowledge graph for a disease might include genes, proteins, metabolites, and drugs as entities, with edges capturing interactions such as regulatory pathways, binding affinities, or disease associations.

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.

Graph-Based Representation

Knowledge graphs typically adopt a triplet-based format, where relationships are explicitly defined using:
Subject → Predicate → Object

Example: (BRCA1 → associated with → Breast Cancer)

This format is a simple, human-readable representation of relationships between entities which offers flexibility but lacks a standardized framework for data integration. It is commonly used for internal knowledge graph implementations that do not require strict schema enforcement.

RDF (Resource Description Framework)

To ensure interoperability and seamless data integration, many knowledge graphs adopt RDF (Resource Description Framework). RDF is a W3C (World Wide Web Consortium) standard designed for structured, linked data.

• Structure: Uses subject-predicate-object triples, just like the triplet-based format, but enforces a formal data model using URIs (Uniform Resource Identifiers) to uniquely define entities.
  
  
• Serialization Formats: Supports multiple serialization formats such as Turtle, N-Triples, RDF/XML, JSON-LD for efficient storage and exchange.
  
  
• Interoperability: RDF enables knowledge graphs to integrate disparate datasets, ensuring consistent mapping across sources like PubMed, UniProt, and DrugBank.
  
  
• Ontology Support: Works with RDFS (RDF Schema) and OWL (Web Ontology Language) to define entity types, relationships, and constraints. Together, RDFS and OWL help define the structure, meaning, and rules of the data, enabling richer semantic understanding, reasoning, and inference.

SPARQL: Querying Knowledge Graphs

Once a knowledge graph is built, specialized query languages like SPARQL (SPARQL Protocol and RDF Query Language) enable researchers to retrieve and explore relationships within the data.

SPARQL functions similarly to SQL, but is optimized for graph-structured data. This capability enables researchers to navigate complex biological interactions, rapidly identifying patterns that would be difficult to extract from traditional databases.

By leveraging graph-based representation, RDF, and SPARQL, knowledge graphs enable seamless data integration across multiple biomedical sources, advanced querying and analysis, and scalability and adaptability, making them suitable for rapidly evolving research fields.

Biomedical Applications of Knowledge Graphs

Knowledge graphs are revolutionizing biomedical research by integrating multi-omics data, clinical records, and scientific literature into a single, interconnected and readable system. Advanced AI techniques, such as Knowledge Graph Embeddings (KGEs), which are vectorized representations of the entities and their relationships, further enhance their predictive power by making knowledge graphs machine-readable, thereby enhancing the rate of discovery of hidden patterns and novel relationships.^[1]

1. Drug Discovery and Repurposing

• Target identification: Mapping protein-protein interactions (PPIs) helps reveal druggable targets. Advanced embedding models like RotatE improve target predictions by capturing complex molecular relationships.
• Drug repurposing: KGEs predict missing links in biomedical networks, suggesting new uses for existing drugs.

Example: The OREGANO knowledge graph integrated multi-omics data and biomedical literature to identify repurposing candidates. It demonstrated high predictive performance in link prediction tasks and successfully highlighted potential treatments for glioblastoma and Alzheimer’s disease, which were supported by existing clinical evidence.^[2]

Similarly, knowledge graphs were instrumental in uncovering Baricitinib, an arthritis drug, as a treatment for COVID-19. This discovery led to Emergency Use Authorization (EUA) by the FDA, followed by full approval as a treatment for hospitalized COVID-19 patients in combination with remdesivir.

2. Biomarker Discovery and Disease Mechanism Insights

Understanding disease pathways and identifying early-stage biomarkers requires integrating data across genomics, transcriptomics, proteomics, and metabolomics.

• Knowledge graphs connect multi-omics datasets, revealing biomarker-disease associations.
• KGEs enhance this by predicting novel biomarker relationships, reducing the time and cost of experimental validation.

3. Personalized Medicine and Clinical Decision Support

By linking electronic health records (EHRs), genomics, and drug response data, knowledge graphs enable precision medicine and better treatment recommendations.

• Predicting patient-specific drug responses based on genetic markers.
• Recommending alternative treatments by analyzing real-world clinical data.

Example: The SPOKE knowledge graph integrates clinical and molecular data to suggest personalized cancer treatments.^[3]

4. Literature-Based Drug Discovery and Hypothesis Generation

With millions of research papers published annually, manually extracting insights is inefficient, time-consuming and biased. NLP-powered knowledge graphs automatically connect concepts across literature to generate new hypotheses.

• KGEs improve relationship predictions by detecting implicit connections between genes, drugs, and diseases.

Example: IBM Watson for Drug Discovery used knowledge graphs to identify new gene-disease links for Amyotrophic Lateral Sclerosis (ALS) by analyzing scientific literature.^[4]

5. Clinical Trial Optimization

Recruiting the right patients for clinical trials is a major bottleneck. Knowledge graphs help:

• Match patient profiles to trial eligibility criteria using EHRs and genetic data.
• Predict trial success rates based on historical data from similar studies.

Example: A recent study demonstrated how a knowledge graph-based approach improved patient cohort selection for Alzheimer's disease clinical trials. By integrating multi-source biomedical data including genetic, clinical, and imaging biomarkers, the model identified optimal patient subgroups, reducing variability and enhancing trial efficiency.^[5] This highlights how knowledge graphs can refine recruitment strategies and improve trial success rates.

6. AI-Powered Insights for Drug Safety and Pharmacovigilance

Post-market drug safety monitoring is critical for detecting adverse drug reactions (ADRs).

• Knowledge graphs integrate FAERS (FDA Adverse Event Reporting System) data, real-world patient records, and scientific literature.
• KGEs help predict potential ADRs before widespread clinical reporting.

7. Antimicrobial Resistance (AMR) and Infectious Disease Research

With AMR rising, knowledge graphs help:

• Predict bacterial resistance patterns based on genomic and epidemiological data.
• Identify alternative treatment strategies by mapping antibiotic mechanisms.

Example: A recent study demonstrated how knowledge graphs enhance antimicrobial resistance (AMR) research by integrating multi-omics data, scientific literature, and clinical reports. The approach enabled the identification of novel resistance mechanisms and helped predict potential antimicrobial compounds.^[6] By systematically linking bacterial strains, resistance genes, and drug interactions, knowledge graphs provide a scalable framework for combating AMR and guiding drug discovery efforts.

Best Practices for Building and Utilizing Knowledge Graphs Effectively

Building a robust knowledge graph requires careful planning, from data integration to query optimization. A well-structured knowledge graph not only relies on data harmonization for its creation, but also serves as a dynamic framework for continuous large-scale data harmonization, enabling efficient discovery and AI-driven insights. Below are key best practices for designing and maintaining an effective biomedical knowledge graph.

1. Structuring a Scalable and Adaptable Ontology

A knowledge graph ontology defines how entities and relationships are categorized and structured.

• Use standardized vocabularies (e.g., MeSH, SNOMED CT, GO, and UMLS) to ensure interoperability.
• Define clear entity types and relationships (e.g., "Gene A regulates Protein B").
• Enable schema flexibility, allowing for the integration of emerging biomedical knowledge without extensive redesign.

2. Ensuring Data Consistency, Quality, and Harmonization

Biomedical data comes from diverse sources, such as scientific literature, clinical trials, genomic repositories, and each of these have varying formats and quality standards.

• Normalize entity names and identifiers using resources like UniProt (for proteins) and PubChem (for chemical compounds).
• Resolve synonyms and ambiguous terms to prevent duplicate entities (e.g., "BRCA1" vs. "Breast Cancer Gene 1").
• Automate data validation through machine learning techniques to detect inconsistencies or missing links.

3. Optimizing for Efficient Querying and Reasoning

A knowledge graph should support high-performance querying, especially in complex biomedical applications.

• Leverage SPARQL or Cypher for fast retrieval of multi-hop relationships.
• Use indexing strategies to optimize traversal speed in large-scale graphs.
• Implement inferencing rules (e.g., if "Drug X inhibits Protein Y" and "Protein Y is involved in Disease Z," then "Drug X may affect Disease Z").

4. Integrating Knowledge Graphs with AI and Machine Learning

A well-constructed knowledge graph enhances AI-driven biomedical discovery by providing structured, relational data.

• Apply knowledge graph embeddings (KGEs) to predict novel drug-target interactions.
• Use graph neural networks (GNNs) for clustering similar disease pathways.
• Incorporate natural language processing (NLP) to extract new biomedical relationships from research papers.

5. Maintaining Scalability and Continuous Updates

Biomedical knowledge is constantly evolving, requiring knowledge graphs to be dynamic and scalable.

• Automate ingestion pipelines to incorporate new findings from literature databases like PubMed.
• Use version control to track data provenance and prevent outdated insights.
• Ensure cloud-based deployment for handling large datasets efficiently.

Why These Best Practices Matter

• Improved data integration ensures seamless connections across diverse biomedical sources.
• Optimized queries and embeddings accelerate hypothesis generation in drug discovery.
• Scalability and adaptability allow for the continuous evolution of biomedical knowledge.

By following these best practices, researchers and organizations can maximize the value of knowledge graphs, unlocking faster insights and better decision-making in life sciences.

Case Study: How Elucidata Helped Build an Advanced Knowledge Graph for AML Target Identification

Elucidata worked with a Massachusetts-based therapeutics company focused on differentiation therapy for Acute Myeloid Leukemia (AML). The company aimed to assess 10 differentiation targets within six months, a process that typically takes 15–24 months. Elucidata helped them build a multi-modal knowledge graph for target-indication assessment, integrating genomic, clinical, and experimental datasets to accelerate discovery.

Challenges Faced

• Difficulty in harmonizing multi-modal data from public and proprietary sources.
• Limited in-house expertise to implement AI-driven search and automation.
• Lack of a structured framework to evaluate differentiation targets across indications and modalities.

Techniques Used by Elucidata

1. AI-Driven Multi-Modal Data Harmonization
   
   
   • We leveraged AI-powered ETL pipelines to ingest and standardize data from over 25 different data types (genomic databases, clinical studies, literature, etc.).
   • We applied entity recognition models to extract relevant biomarkers, tumor types, and therapeutic targets.
   • We ensured 99.99% data accuracy through scalable harmonization and human-in-the-loop curation.
     
     
2. Graph-Based Target-Indication Assessment
   
   
   • We used Polly GraphAI, an advanced knowledge graph system, to integrate multi-omics data.
   • We enabled cross-ontology search and link prediction to identify new disease pathways.
   • We provided a 360-degree view of targets and their biological context.
     
     
3. AI-Enabled Search and Advanced Data Visualization
   
   
   • We used graph analytics to map gene-disease-drug relationships.
   • We provided mutation heat maps, pathway networks, and survival curves to assist researchers in decision-making.
   • We summarized targets based on biological relevance, druggability, and competitive intelligence.

Impact of the Knowledge Graph Approach

• Four times faster target-indication assessment, reducing the timeframe from 24 months to just 6 months.
• Three times reduction in costs for generating custom target reports.
• Increased in success probability from 1:2000 to 1:5, significantly improving early discovery outcomes.

By implementing a scalable, AI-powered knowledge graph, Elucidata helped the therapeutics company make faster, data-driven decisions, accelerating drug discovery for AML.

Conclusion: Unlocking the Power of Knowledge Graphs in Biomedical Research

Knowledge graphs have transformed the way biomedical data is structured, analyzed, and leveraged for discovery. By integrating diverse datasets and uncovering hidden relationships, they enable researchers to accelerate hypothesis generation, improve target identification, and streamline drug development. However, building and maintaining a scalable, high-quality knowledge graph requires expertise in data harmonization, ontology design, and AI-driven analytics.

Elucidata specializes in constructing advanced biomedical knowledge graphs tailored for life sciences research. Our AI-powered data harmonization platform, Polly, enables seamless integration of multi-omics, clinical, and experimental data, empowering researchers with deeper insights and faster discoveries. Whether you’re looking to optimize target identification, accelerate drug repurposing, or enhance biomedical AI models, Elucidata’s expertise can help you scale your research.

Ready to transform your biomedical data into actionable insights? Get in touch with Elucidata today.