8 Modern Biomedical Applications That Can Leverage Biomedical Knowledge Graph in 2023

In modern biomedical technology, knowledge graphs have undergone significant evolution, presenting new opportunities across genomics, pharmaceuticals, and clinical applications. These dynamic knowledge frameworks have the potential to revolutionize the integration and analysis of extensive biological data, paving the way for advancements in healthcare

In this article, we will discuss eight diverse fields of biomedicine and discuss how they can leverage knowledge graphs to augment their contributions to healthcare in the coming future.

8 Modern Biomedical Applications That Can Be Powered By Biomedical Knowledge Graphs 

Here are eight modern biomedical applications where biomedical knowledge graphs have the potential to make a significant impact:

1. Microbiome Study

Microbiome is a subdomain of biomedical research that involves studying the behavior, interactions, and functioning of microbial communities called microbiota. These communities consist of highly interactive microorganisms residing in groups in a specified environment, like the human gut. Understanding all the genetic material within a microbiota can help us understand how they affect human bodies.

Owing to rapid advancements in biomedical applications, biomedical researchers have gathered a plethora of information about these microbiomes and their existence as a part of an ecological community. Technologies like knowledge graphs significantly reduce the time and cost of analyzing this wealth of microbiome data.  

In-depth knowledge about microbiomes enables biomedical researchers to find new ways to engineer microbiota to help create new methods to diagnose diseases, understand organ function, and repair wounds. Knowledge graphs’ effective data management capabilities make it easier for researchers to gain valuable insight into the interactions between different microorganisms and their effects on their host environment. 

For instance, scientists can use knowledge graphs to track how various gut microbes in babies react when they start eating solid foods. This will be helpful in understanding and optimizing an infant’s diet for better gut health. 

For reference, here is a schematic illustration of microbial co-occurrence networks for the infant gut microbiome based on association graph.

Source: Emerging priorities for microbiome research

2. Studying Biological Mechanisms That Regulate Cellular Behavior

Molecular biology is a complex biomedical research field involving the analysis of cellular biology and its underlying processes. Cells are a composition of a network of tiny molecules, like DNA, RNA, proteins, and metabolites. They interact to control how the cell behaves. Understanding the interplay between these biomolecules can be beneficial for biomedical researchers to develop new drugs and find ways to treat diseases. 

Over the years, researchers have accumulated large volumes of experimental biomolecular data in their efforts to analyze cellular networks. However, modeling these networks is a complex feat that requires large-scale data integration and management. This is where knowledge graphs can help study the biological mechanisms that regulate cellular behavior.

Here is an example of the types of nodes and relationships in a biomedical knowledge graph for studying biological mechanisms:

Metagraph of a molecular knowledge graph (Source: Predicting disease-gene associations)

Biomedical knowledge graphs can put years of molecular research to good use by providing a platform to capture and centralize all relevant information for an in-depth analysis. Researchers can gain a holistic view of the complex underlying biomolecular interactions by analyzing data from multiple sources, such as genomics, epigenomics, transcriptomics, proteomics, and microbiomics. 

This information can be leveraged to: 

  • Predict how pairs of proteins will interact
  • Determine the function of a specific protein
  • Predict the 3D structure of proteins

For example, knowledge graphs can prove revolutionary for complex disorders like Alzheimer’s disease. Using knowledge graphs for identifying genes and biological communities involved in Alzheimer’s could pave the way for effective patient stratification strategies.

3. Gain-of-Function Research

Gain-of-function (GOF) research in medical science involves genetically modifying an organism to enhance the biological functions of its gene products. Primarily conducted in virology, GOF studies aim to investigate how pathogens adapt to environmental pressures, enabling a better understanding of disease control strategies, potential vaccines, and therapeutic interventions. 

By manipulating the genetic makeup of pathogens, researchers can uncover intricate details about viral transmission and replication mechanisms. GOF research data provides valuable insights into the behavior and characteristics of pathogens, paving the way for advancements in combating infectious diseases and improving public health outcomes.

One of the challenges in GOF research is integrating and interpreting diverse and complex datasets from different formats, databases, or research publications, making it challenging to consolidate and make sense of the information. Knowledge graphs can solve this challenge by providing a unified framework for integrating and organizing diverse data. They can enable the exploration of complex relationships, identification of patterns, and extraction of insights that may not be apparent when analyzing individual datasets in isolation. 

Knowledge graphs can be instrumental in GOF research for visualizing and analyzing possible mutations that can occur in known viruses. For instance, in building a COVID-19 knowledge graph to prepare for future pandemics, researchers can integrate genomic data, protein structures, epidemiological information, and clinical findings into a unified graph representation. Visualizing the Covid mutations and their connections within the knowledge graph can enable researchers to gain insights into the potential emergence of novel variants, assess their potential risks, and inform the development of preventive measures, including vaccines, therapies, and surveillance strategies. 

The following illustration depicts the entities and relationships involved in repurposing drugs for various viral mutations:

Schema of drug repurposing KG for Covid (Source: DRKG)

4. Facilitate Bioengineering Study

Bioengineering is a multi-faceted biomedical application that merges engineering, biology, and medicine principles to create innovative technologies and solutions for enhancing human health and well-being. Bioengineers develop new tools, techniques, and therapies that advance medical diagnostics, treatment methods, and overall healthcare by applying engineering principles to biological systems. Researchers explore diverse areas such as synthetic biology, biomedical imaging, tissue engineering, biomolecular engineering, and beyond. 

Like any other research field, bioengineering deals with a plethora of biomedical information, withholding valuable insights. Biomedical knowledge graphs can facilitate bioengineering studies by helping researchers overcome knowledge management challenges and enhance the efficiency and effectiveness of their bioengineering research endeavors. Knowledge graphs provide a platform for hypothesis generation and exploring research directions based on existing knowledge and discoveries, fostering innovation in the field.

A biomedical knowledge graph can help visualize the complex connections between biological entities for uncovering valuable insights. Here is a schematic representation of what that can look like: 

A general biomedical knowledge graph (Source: Healthcare Knowledge Graph Construction)

For example, knowledge graphs can aid bioengineering studies in improving personalized medicine. Knowledge graphs provide a framework for gathering and analyzing complex patient-specific data, enabling bioengineers to uncover individualized insights, develop personalized treatment strategies, and drive advancements in precision medicine. 

5. Optimize Clinical Trials With Accelerated Timelines

Clinical trials are conducted to evaluate the safety and success of interventions, treatments, or investigational drugs on human subjects. These trials are pivotal in developing new medicines and advancing innovative treatments. By rigorously testing and assessing the outcomes, clinical trials provide essential evidence for regulatory approvals, ensuring the introduction of safe and effective therapies to improve patient care and advance medical science.

While successful clinical trials are essential to the development of drugs, they are generally very costly to conduct and highly likely to fail. An innovative solution to this problem is using technologies like knowledge graphs that can centralize large volumes of clinical information and enable quick data mining. By consolidating diverse datasets into a unified graph, knowledge graphs provide a comprehensive and structured representation of clinical knowledge, allowing the researchers to efficiently access and analyze a wealth of information and quickly uncover hidden insights such as drug outcomes or risk factor predictions. 

Moreover, they can facilitate the comparison of characteristics among historical drugs and use historical data to build forecasts for new drug designs. Biomedical researchers can rapidly make informed decisions on sensitive treatment options by visually exploring correlations between medical entities. This streamlined process reduces the time required for data collection, analysis, and decision-making, ultimately optimizing clinical trials with accelerated timelines and bringing new treatments to patients more swiftly.

The CTKG is a publicly available knowledge graph for clinical trials. It aids medical researchers in designing innovative clinical trials for drug repurposing, similarity search, and more. Here is a schema of CTKG showing the information entities and relations involved in clinical trials.

Schema of CTKG (Source: CTKG: A knowledge graph for clinical trials)

6. AI-Based Decision Support Systems

AI-based decision support systems play a crucial role in modern healthcare by leveraging the vast amounts of structured, unstructured, and semi-structured data collected from various touchpoints. These biomedical applications employ advanced algorithms and machine learning techniques to analyze and understand complex healthcare data, enabling providers to make critical clinical decisions daily. 

By identifying patterns, anomalies, and unusual information from medical data, AI-based decision support systems: 

  • Help healthcare professionals diagnose diseases
  • Predict patient outcomes
  • Recommend personalized treatment plans
  • Improve overall patient care 

These AI systems heavily rely on the quality of their knowledge base for augmenting human expertise, enhancing accuracy, and facilitating evidence-based decision-making. This is where knowledge graphs shine. 

By leveraging knowledge graphs, decision support systems can leverage a rich and connected knowledge base that can power them to make clinical decisions with high precision. Knowledge graphs provide a transparent and interpretable representation of patient information. It allows healthcare professionals to understand the underlying relationships and reasoning behind AI-based recommendations. 

Visualizing connections and contextual information within the graph makes it easier to explain and justify the decisions made by AI systems, enhancing trust and acceptance among healthcare providers. As a result, healthcare providers can visualize all possible decision paths to help them narrow down to informed medical decisions.

For example, biomedical knowledge graphs can be integrated with eXplainable AI (XAI) to create an explainable framework for an AI-based clinical decision support system. It can be beneficial to healthcare decision-makers in making better clinical decisions benefiting both patients and caregivers. 

The following diagram shows how placing knowledge graphs at the center of clinical decision support systems can increase the level of explainability of outputs.

Source: A Knowledge Graph-Based Explainable Decision Support System in Healthcare

7. Medical Diagnosis Powered by Large Language Models & Knowledge Graphs

Medical diagnosis determines the nature and cause of a patient’s illness or condition. It thoroughly assesses symptoms, medical history, physical examinations, and diagnostic tests. 

Accurate medical diagnosis is essential for effective treatment planning, as it guides healthcare providers in selecting appropriate therapies, interventions, and medications. To ensure proper and timely healthcare delivery, researchers can use knowledge graphs and large language models like Med-PaLM 2 to power medical diagnosis. 

The language understanding capabilities of large language models, with the structured and interconnected knowledge representation of knowledge graphs, can make an excellent combination for advanced medical diagnosis. These models can help build biomedical applications that can quickly analyze large amounts of medical literature, patient records, and clinical guidelines to assist healthcare professionals in accurate and timely diagnosis. The contextual understanding and knowledge captured within these models and graphs offer more comprehensive and informed decision-making, leading to improved patient outcomes and more efficient healthcare delivery.

Here’s a depiction of knowledge graphs and large language models working in conjunction, powering a computational approach to retrieving insights for medical diagnosis.

For instance, knowledge graphs and large language models can be beneficial in diagnosing rare genetic diseases. They can help identify potential rare disease candidates, guide genetic testing, and facilitate collaboration among specialists, ultimately leading to quicker and more accurate diagnoses for patients with rare conditions.

8. Healthcare Data Interoperability To Support Interdisciplinary Research

Healthcare data interoperability refers to the ability of different healthcare systems, applications, and devices to seamlessly exchange and use healthcare data. It involves the standardized representation, format, and sharing of patient information across different healthcare settings and organizations. 

Achieving healthcare data interoperability is crucial for ensuring the continuity of care and improving clinical decision-making. However, the lack of standardized healthcare data formats and the differing governance policies of various institutions can significantly hinder the seamless exchange of information between healthcare systems. 

Knowledge graphs can offer valuable support for healthcare data interoperability, particularly in facilitating interdisciplinary research. They provide a standardized and structured healthcare data representation, capturing relationships and semantic meaning. They are a central repository for integrating data from diverse sources and domains, allowing seamless data exchange. 

Knowledge graphs harmonize different data formats, coding systems, and terminologies through mapping techniques. Additionally, they enable semantic understanding and inference capabilities, aiding in data integration and facilitating interdisciplinary collaboration. 

Biomedical researchers can access and analyze integrated healthcare data from multiple disciplines by leveraging knowledge graphs. It can help them in fostering multidisciplinary research efforts and driving insights for improved patient care and outcomes.

For example, consider the field of precision medicine that combines genomic data, clinical information, and other relevant data to provide personalized healthcare. By constructing a standardized graph capturing the relationships between genes, diseases, drug responses, and patient characteristics, interdisciplinary researchers can access a comprehensive and interconnected knowledge base. This aids them in holistically identifying patterns that help in disease prediction, early intervention, and personalized treatment approaches.

Accelerating Biomedical Innovation With Wisecube’s Knowledge Graph Engine

Biomedical data is growing exponentially, and it is no longer humanly possible to keep up with it. Collecting, structuring, and integrating this data is highly challenging. Also, it is time and resource-intensive.

At Wisecube, we solve these challenges with Orpheus: Our Biomedical Knowledge Graph, the most extensive knowledge graph of its kind, containing billions of facts about millions of biomedical entities, integrated from thousands of sources using our cutting-edge AI. With the expanding scope of biomedical applications, Orpheus can serve as an invaluable tool for knowledge management.

Contact us today to learn more about accelerating your scientific discovery with our biomedical knowledge graph.

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