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The global big data in healthcare market is projected to grow from $67 billion in 2023 to $540 billion by 2035, with a CAGR of 19.06% during the forecast period of 2023-2035, according to the Big Data in Healthcare Market report. However, despite the increasing demand for proper data analysis, the biggest challenge remains the fact that a significant amount of this crucial data is scattered and disorganized.
This is where knowledge graphs come in handy. They can integrate both structured and unstructured data, linking important business and scientific concepts, which ultimately improves clinical outcomes and reduces bottlenecks.
In this article, we will explain what a knowledge graph is and how they are built, while also providing five applications of knowledge graphs in the life sciences industry.
A knowledge graph is a powerful tool used to represent complex and interconnected data through a graph structure.
In essence, a knowledge graph consists of nodes representing entities (such as proteins, genes, diseases, etc.) and edges representing the relationships between these entities. This graph-based representation allows for the integration and interlinking of vast amounts of data from various sources, driving easier querying and understanding of the relationships and interactions within the data.
In the life sciences, knowledge graphs are used to manage and interpret the vast amounts of relational data produced by research efforts. They enable the semantic description of real-world entities through ontologies, which are structured frameworks that define the relationships and categories within a domain. These ontologies help standardize the representation of data, making it easier to integrate and analyze information from different sources.
Source: https://www.nature.com/articles/s41597-024-03171-w
Above is an example of a knowledge graph by a group of life science researchers which represents the levels of biological organization underlying human disease. At a high level this knowledge graph represents anatomical entities (tissues, cells, and bodily fluids) containing genomic entities such as DNA, RNA, mRNA, and proteins. It simplifies extremely complex interconnections, enabling informed analytical and medical decisions.
Building a knowledge graph involves several key phases, each critical to the construction and maintenance of a robust and useful visual representation. The main phases are broken down below.
The first phase includes identifying and selecting data sources that will be integrated into the knowledge graph. These sources can include structured databases, semi-structured documents like XML, and unstructured text from scientific publications.
The next phase includes developing a controlled vocabulary and schema to describe the data. This can be done through top-down approaches, which involve expert input and existing ontologies, or bottom-up approaches, which use automated methods to build ontologies from data.
Phase three consists of using techniques such as Natural Language Processing (NLP) and text mining to identify and extract entities and their relationships from the selected data sources.
This phase includes integrating the extracted entities and relationships into the knowledge graph, resolving ambiguities and redundancies, and aligning them with the ontology to ensure consistency and accuracy.
Next comes storing the knowledge graph in a way that supports efficient querying and analysis, often using graph databases or triple stores.
Lastly, one must continuously update the knowledge graph to reflect new data and discoveries, ensuring that it remains current and relevant.
Knowledge graphs offer numerous benefits, particularly in the life sciences, where the complexity and volume of data can be overwhelming. Some of the key benefits include:
Knowledge graphs enable the seamless integration of data from multiple sources, providing a unified view that can be easily queried and analyzed. This is particularly valuable in fields like pharmacogenomics and ecotoxicology, where diverse data types and sources need to be combined.
By structuring data in a graph format, knowledge graphs facilitate advanced data mining and machine learning techniques that can uncover new relationships and insights. For example, knowledge graphs can help predict drug interactions or identify potential new uses for existing drugs.
Knowledge graphs enhance the interpretability and explainability of AI models by providing clear, structured representations of the data. This is crucial in healthcare and other life science applications where understanding the rationale behind AI decisions is essential for trust and adoption.
Knowledge graphs support efficient data storage, retrieval, and visualization, making it easier for researchers to navigate and make sense of complex datasets. Tools like Neo4J and RDFox offer robust solutions for managing large-scale knowledge graphs.
Knowledge graphs are inherently scalable, allowing for the continuous addition of new data and knowledge. This ensures that the knowledge graph remains a valuable resource as scientific understanding evolves.
Kanda Software specializes in creating custom solutions for life sciences organizations. Our expertise in integrating complex data through advanced technologies, like knowledge graphs, ensures that your organization can effectively manage and utilize vast amounts of scientific data. By partnering with Kanda, you can streamline your data processes, enhance research outcomes, and drive innovation in your projects. Contact us today to discover how we can tailor our services to meet your specific needs and elevate your data management capabilities.
Below are five applications of knowledge graphs in the field of life sciences, demonstrating their impact in deciphering complex data concepts.
Knowledge graphs like PrimeKG integrate diverse biomedical data, including electronic health records, clinical trials, and genomic data, to provide a holistic view of diseases. This integration supports the development of personalized diagnostic strategies and targeted treatments by mapping connections between molecular and genetic factors and their phenotypic outcomes. For example, PrimeKG includes 17,080 diseases with over 4 million relationships representing different biological scales, aiding precision healthcare research.
Source: https://zitniklab.hms.harvard.edu/projects/PrimeKG/
Knowledge graphs are supporting the development of new drugs, therapies, and medical devices. By integrating data from various sources—such as chemical structures, gene expression data, clinical trial outcomes, and genomic information—they help reveal hidden relationships between disparate data points. This capability not only poses new research questions but also drives interdisciplinary collaborations, a solid foundation for groundbreaking discoveries.
By integrating patient data such as medical history, lab results, and imaging studies, knowledge graphs provide a comprehensive view that helps in accurate disease diagnosis and management. This is particularly beneficial in managing chronic diseases, mental health issues, and rare diseases.
For example, The Genetic and Rare Diseases (GARD) Information Center has developed an integrative knowledge graph that combines various data sources, such as OMIM (Online Mendelian Inheritance in Man), a comprehensive, authoritative compendium of human genes and genetic phenotypes, and Human Phenotype Ontology, to support biomedical research on rare diseases. This graph helps map orphan drug designations to specific rare diseases, assisting in the understanding and treatment of these conditions.
In the example knowledge graph below is a demonstration of potential disease pathogenesis discovery for rare diseases (large yellow nodes denote GARD diseases; small yellow nodes denote conditions; purple nodes denote drugs; red nodes denote chemicals).
Source: https://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-020-00232-y/figures/3
In the field of medical imaging, knowledge graphs can consolidate and analyze data from MRI, CT scans, and other imaging modalities. By mapping imaging data into a graph format, researchers can uncover complex relationships between data points, leading to the development of innovative imaging techniques and diagnostic algorithms. This enhanced understanding can greatly advance medical imaging research and clinical practices.
Knowledge graphs improve health information management by analyzing data exchange protocols, governance, and standardization. This reduces data silos and enhances communication across systems, allowing researchers to identify relationships and generate new insights more effectively (Broad Institute).
This article has explained what a knowledge graph is, how they are built, and five applications in the life sciences industry.
As the volume of life sciences data continues to grow exponentially, knowledge graphs provide straightforward access to complex connections and interdependencies, driving the industry forward.
Kanda Software is at the forefront of this transformation. We offer cutting-edge solutions tailored to meet the unique needs of organizations in this domain. With our expertise in implementing custom innovative solutions, your organization can leverage the full potential of your vital data, regardless of its complexity and volume.
Still struggling to connect your data with business objectives? Talk to our experts to find a transformative solution today!
