The age of big data has brought a massive influx of information into the healthcare sector, but that information is still underutilized due to the analytical complexities. A Knowledge Graph (KG) solves this problem by providing new inferences and human-understandable explanations of complex data.
A knowledge graph represents real-world factual information in structured graphs. Simple factual information consists of two entities (concepts) and their relationship with each other (fact). Knowledge graphs provide context to complex data by interlinking it and identifying relationships among different entities.
This blog will explore biomedical knowledge graphs and how they can help the healthcare industry capitalize on data. We will also briefly go over how a biomedical knowledge graph can be created and discuss its applications in healthcare.
What Are Biomedical Knowledge Graphs?
The latest research on biomedical knowledge graphs defines it as a resource that unifies one or more specialized data sources into a graph where nodes represent biomedical entities and edges represent relationships between two entities.
Biomedical graphs enable a semantic search of biomedical data and provide clear insights to health professionals, enabling them to make informed decisions. These graphs also facilitate research and the discovery of better drugs and treatment plans.
Why Use Knowledge Graphs in Healthcare?
The rise and availability of data have brought improvement to healthcare. But, with the continuous aggregation of data, it became difficult for healthcare professionals to manage and take advantage of it.
Graphs provide a very simple and convenient representation of information. Some of the key factors why KGs are so important are:
- KGs help map relationships between an enormous variety and structures of healthcare data.
- KGs provide the capability to model latent relationships between information sources and capture linked information (i.e., entity relationships) that other data models fail to capture.
- Health professionals can easily find information from various variables and data sources.
Building Biomedical Knowledge Graphs
To build a biomedical knowledge graph, the most important steps are:
- Clarity of Purpose for the Biomedical KG
The first thing to do is clarify the exact purpose of the KG. The scope of the study needs to encompass only the use case it is based on. For example, to approach the drug discovery of a disease, the disease, and the target molecule need to be defined.
Hypothetically, in drug discovery of COVID-19, we could first identify the target molecule we want the drug to act on and then identify questions like:
- What are the attributes of the target molecule?
- Which existing drugs act on the same target molecule?
- Which symptoms do these drugs cater to?
- Data Collection
Data is either taken from a pre-existing source or extracted in one of two ways:
- Using AI and ML techniques like text extraction to extract relationships
- Manual curation by reading papers and annotating sentences that assert a relationship
Most of the time, relevant data is already available in a pre-existing database, and the time-consuming task of data collection is reduced by accessing the available data and tweaking it according to the requirements.
Many databases are available for biomedical data that could suit the use case.
- Data Cleaning for Better Data Quality
The collected data is then cleaned for the particular use case. In the use case of COVID-19, all data relating to viral infections causing symptoms like body aches, high fever, dry cough, breathing problems, etc., could be assembled.
- Creating the Data Model with Ontologies
Ontologies are data models representing concepts within a domain and their relationships with each other. Ontology modeling adds semantics to the biomedical KGs by describing the structure of the information in the domain.
In this process, there are three knowledge extraction steps:
- Entity extraction—the subject under discussion is extracted/identified in this step.
- Relationship extraction—the predicate is identified in this step, which identifies the relationship between the subject and its attribute.
- Attribute extraction—the object or quality of the entity is identified in this step.
Applications of Biomedical KGs in Healthcare
Biomedical KGs can help researchers and doctors find critical insights into many biomedical problems like drug discovery, disease diagnosis, health management, etc. We now look closely at some of the most important applications of biomedical knowledge graphs.
- Detection of Healthcare Misinformation with KGs
When COVID-19 broke out, it was mostly an unknown disease. Medical fraternity across the globe relied on observations and inputs from COVID patients for information. It presented an enormous challenge of separating misinformation from the data on the disease. To end that, a knowledge-graph-based framework was developed that specifically modeled COVID-19 knowledge facts to detect misinformation.
- Preserving Healthcare Privacy
Hospitals and healthcare centers share medical data among themselves for a better disease understanding. This data carries sensitive information that has to be preserved. However, withholding information can be fatal in disease management. KGs, with the help of anonymization, can be used to effectively share knowledge without jeopardizing data privacy.
In anonymization with KGs, the patient’s identity, e.g., their name, is replaced with pseudonyms, and generalized range attributes can substitute numerical attributes like age. Other generalizations of categorical attributes can also be made, and the links can be added in a random manner to keep the knowledge graph anonymous.
- Explaining Mental Disorders
Knowledge graphs explain and find significant insights into several mental disorders. Some of the applications in this field include:
- Explanation of autism spectrum disorders.
- Study and association mapping of depression disorders.
- Analysis for new insights, i.e., metabolism-depression associations
- Medical Imaging Analysis
Medical Imaging Analysis uses medical and biological images to find new medical insights via computational methods. Knowledge graphs facilitate medical imaging analysis.
A recent study on the implementation of KGs in this field presented that KGs are effective in the following phases of medical imaging analysis:
- disease classification
- disease localization and segmentation
- report generation
- image retrieval
- Multi-omic Research
Researchers use knowledge graphs to study biological molecules in a multi-omic approach. In this approach, scientists combine and analyze the data of omic groups that include genomics, epigenomics, transcriptomics, proteomics, and microbiomics.
KGs, with the integration of AI methods, have helped in multi-omic discoveries, helping researchers establish connections between omic groups and diseases.
- Knowledge graphs integrated with recommendation systems help establish links between RNA with diseases and proteins with other proteins.
- An algorithm called collaborative filtering helped establish an association between miRNA and diseases.
- Advanced Machine Learning (ML) techniques with KGs help infer associations between genes and disease symptoms.
- Drug Discovery
The problem of optimal drug prescription is not just dependent on the disease or the patient’s condition but also several other factors like demographics, drug availability, availability of drug specialists, etc.
KGs are used to make it easier to cater to these complexities. Researchers have integrated medical knowledge bases into a KG that could help everyone in the domain. By integrating with AI methodologies, researchers are using KGs for drug discovery.
- Drug Repurposing
In drug repurposing, existing medicines are chosen to treat new diseases. When COVID-19 emerged, drug repurposing was done to find out the best possible drugs to treat the dominant effects of the viral infection.
KGs are very important in easing the process of drug repurposing by providing intelligent methods to find solutions to existing treatments. Researchers use structured and unstructured medical data to construct actionable KGs for drug repurposing.
- Adverse Drug Reactions (ADRs)
ADRs are unwanted and dangerous drug effects that can endanger the patient’s life. KGs are used in the research for efficient prediction and possible mitigation of ADRs.
- Patient Diagnosis
During a patient’s medical assessment, diagnosis of the disease can be a complex task, especially if the symptoms relate to various diseases and/or due to comorbidity. A knowledge graph—encompassing disease knowledge, symptoms data, and diagnosis paths—helps in the accurate diagnosis of the disease.
- Healthcare Management
In healthcare management, especially when a patient suffers from a chronic disease, KGs address and manage critical health-related issues. Several food- and diet-related KGs have been devised to help patients with food selections and get control over their nutrition to better deal with chronic disease.
- Integration of Medical Knowledge
One of the most significant applications of KGs is that they facilitate integrating and explaining the huge amount of diverse medical data healthcare organizations take in daily.
Medical data is abundantly present in textual form and challenging to interpret and draw inferences from. Knowledge graphs contextualize this complex data by unifying it into logical graphs.
Optimize Biomedical KGs with Wisecube’s Knowledge Graph Engine
Knowledge graphs bridge the gap between healthcare and big data by making it easier to draw significant inferences from biomedical data. The healthcare industry will improve further by integrating AI techniques in the data curation and relationship extraction processes. Wisecube offers a straightforward solution with a Knowledge Graph Engine that centralizes biomedical data and orchestrates human-understandable insights for complex problems.
If you are looking to accelerate your biomedical research, or contribute toward healthcare advancements effectively, schedule a call with us today and get started.