Harnessing Knowledge Graphs For Advancements In Life Sciences

Amid today’s ever-evolving technological landscape, AI is going through a pivotal transformation. This advancement targets the ‘context gap’ within AI applications, a crucial aspect for solving complex business challenges. At the heart of this advancement is integrating knowledge graphs, enabling machines to fuse data-driven learning and knowledge-based reasoning – a crucial transition heralding the era of Hybrid Intelligence in AI.

In the life sciences domain, this hybrid intelligence plays a transformative role by merging data-driven learning and knowledge-based reasoning facilitated by knowledge graphs.

In this article, we will highlight the pivotal role of knowledge graphs in refining extensive volumes of life sciences data. We will also discuss how they can be leveraged for a structured and interconnected approach to fueling a variety of life sciences use cases.

What are Knowledge Graphs & How Do They Work?

A knowledge graph is a graphical network of information that leverages graph and AI technologies to unify and organize large volumes of structured and unstructured data. It is a comprehensive web of interconnected nodes and edges, built upon existing knowledge bases, portraying a holistic view of real-world entities and their relationships within a specific domain. Unlike traditional relational data models, the knowledge graph data model offers a visual representation of intricate data interconnections mirroring the complex neural connections of human intelligence.

To emulate the cognitive processes of the human brain, knowledge graphs incorporate two fundamental learning systems, empowering them to effectively infer and reason with real-world data: Logic-based reasoning and Intuition-based learning.

  • Logic-Based Reasoning System

The interconnected structure of nodes and edges in a knowledge graph functions akin to the information processing of the human brain, drawing on graph theory. This framework allows the graph to mirror real-world connections, facilitating logical reasoning within the data it encompasses.

For instance, in a medical knowledge graph, a patient’s symptoms (cough, fever, shortness of breath) are linked to diseases (pneumonia, bronchitis, COVID-19). Employing logic-based reasoning, the graph can deduce a potential COVID-19 diagnosis by analyzing these relationships. This mimics the logical conclusions drawn by medical professionals based on symptom profiles and disease associations.

  • Intuition-Based Learning System

Knowledge graphs transcend their role as mere data repositories by employing technologies like machine learning and AI to function as intelligent systems capable of understanding the information they contain. This approach enables them to deliver contextual comprehension, facilitating intuition-based learning comparable to human cognitive capacities.

For example, consider a healthcare knowledge graph supported by AI where standard connections are found between symptoms like persistent cough, shortness of breath, and exposure to a specific industrial chemical, often leading to diagnoses related to respiratory issues. Suppose a new patient presents similar symptoms and a history of exposure to this chemical. In that case, using intuition-based learning, the knowledge graph might swiftly recommend exploring respiratory conditions linked to chemical exposure, helping healthcare professionals consider potential diagnoses more effectively.

Life Sciences Background and Data Challenges

Life sciences is a multi-faceted industry branched into diverse sectors, including biomedicine, pharmaceuticals, biotechnology, and more. Each of these sectors focuses on distinct aspects of researching and developing solutions for living organisms. The rich tapestry of life sciences information spans an extensive array of datasets ranging from genomics, proteomics, and molecular research to drug development records, patient profiles, clinical trials, and many more.

Despite the abundance of available data, the intricate interdisciplinary landscape of life sciences presents several challenges in comprehending and effectively utilizing this wealth of information.

  • Decentralized Data: This decentralized nature of valuable life sciences information complicates data access, analysis, and correlation, hindering a comprehensive data view.
  • Siloed Information: Valuable data signals are scattered across datasets without a unified linkage, leading to isolated data silos. This fragmentation hinders the extraction of comprehensive insights from real-world research, as valuable connections and correlations remain buried within isolated datasets.
  • Interoperability:  Every life sciences sector operates within its domain of expertise. However, interdisciplinary research often requires collaboration and data correlation across these specialized fields, making data interpretation and integration complex.
  • Integration Challenges: Harmonizing and linking disparate datasets for cohesive analysis and drawing correlations is a significant challenge due to differing data formats, structures, and standards across sectors and organizations.

Resolving Life Sciences Data Obstacles With Knowledge Graphs

The complexity of siloed life sciences data demands a critical need for integrating and unifying diverse information sources to bridge gaps between different areas of study. This is where knowledge graphs step in as a robust solution, offering an integrated platform for the life sciences industry to overcome its hurdles and attain cohesive insights.

Structured Framework for Interconnected Biological Entities

The foundational architecture of a knowledge graph’s schema plays a pivotal role in representing complex interconnections among biological entities in the life sciences domain. This schema serves as a structured framework, facilitating a comprehensive mapping of connections among genes, proteins, pathways, and diseases, addressing the challenge of scattered and disconnected data in the life sciences field. Moreover, it allows for a thorough understanding of biological mechanisms and aids in exploring relationships within complex life sciences data, mitigating the challenges of fragmented and siloed information.

A simplified schema of the Wikidata Knowledge Graph

Enabling Data-Driven Research and Decision-Making

The structured approach of knowledge graphs assists researchers in uncovering and interpreting relationships within the extensive web of life sciences data. This detailed representation of entities, their attributes, and intricate relationships within the knowledge graph enhances comprehension of biological mechanisms, aiding data-driven research, and facilitating informed decision-making in the life sciences.

Empowering Data Integration and Validation

Knowledge graphs enable cross-referencing and validation of AI predictions against real-world evidence, enhancing the credibility and accuracy of research outcomes in the life sciences. By seamlessly linking predictions, such as a drug compound’s efficacy, to existing clinical trial data, genetic information, and historical research findings, knowledge graphs offer a platform for validating complex AI-driven insights.

Transformative Impact of Knowledge Graphs in Life Sciences

In the complex terrain of unstructured and dispersed data, knowledge graphs shine as a beacon of coherence within the life sciences field. This framework serves as a vital means to harmonize and delve deeper into the complexities of knowledge of life sciences. The utilization of knowledge graphs extends to several critical use cases within the life sciences domain, including:

Structured data solution for unstructured life sciences data

Utilizing a structured approach, knowledge graphs provide a solution for the challenge of unstructured life sciences data. By organizing fragmented details regarding genes, proteins, diseases, and treatments, these graphs streamline and bring coherence to this complex web of information. This structured format acts as a navigational aid, facilitating efficient navigation through intricate biological networks, allowing for comprehensive analysis, and unveiling hidden patterns within extensive clinical and biological datasets.

A general biomedical knowledge graph structure

This structured approach finds applications in critical areas such as drug development, disease understanding, and personalized medicine. For instance consider a biomedical knowledge graph that organizes information on drug compounds, their molecular properties, and disease pathways, streamlining the identification of potential drug targets. Researchers can efficiently discern relationships between specific molecular elements and disease mechanisms, expediting the discovery of novel interventions. This structured understanding accelerates the development of innovative therapeutics, offering promising avenues for combating complex diseases.

Semantic associations for enhancing clinical research

In the clinical study landscape, where disjointed datasets pose a hurdle, knowledge graphs harmonize diverse sources, integrating patient profiles, treatment outcomes, disease mechanisms, and clinical trials. By creating semantic relationships between various medical concepts, these graphs facilitate a holistic understanding of patient health profiles, treatment efficacy, and disease mechanisms, thereby enriching insights for clinical decision-making and medical research.

Sample Clinical Trial Knowledge Graph 

With an enhanced semantic understanding, knowledge graphs empower critical clinical applications such as pharmacovigilance, clinical trial design, or even rare disease identification. For instance, consider a scenario involving a drug used to manage hypertension. By highlighting the semantic relationships connecting the drug compound, patient medical records, and reported side effects, a knowledge graph can highlight potential rare adverse reactions related to liver function abnormalities in patients taking this medication. Healthcare professionals can then promptly monitor and prevent adverse effects in patients prescribed this drug, ensuring timely intervention and improved patient safety.

Holistic view for insightful literature reviews

Amidst the vast and diverse life sciences literature, knowledge graphs provide a comprehensive view by contextualizing and interlinking various biomedical publications. This allows researchers to gain deeper insights into scientific knowledge and its connections, assisting in identifying research gaps and providing cross-disciplinary insights essential for literature navigation and exploration.

Sample knowledge graph depicting a 360 view of human diseases mechanisms

Consider a knowledge graph tailored for Alzheimer’s disease, linking genetic studies, disease mechanisms, and research articles. This graph not only identifies research gaps but also provides a comprehensive overview of how genetic mutations are addressed in various studies. This approach aids in formulating hypotheses by contextualizing the genetic mutation’s relevance within diverse studies, thereby advancing understanding of the disease’s genetic factors and potential treatment avenues.

Concept linking for expedited drug discovery

In the disconnected information terrain of biomedicine, knowledge graphs can be used for concept linking to streamline the drug discovery process. This approach links complex molecular structures, pharmacological properties, and disease pathways into an interconnected data network. It enables knowledge graphs to identify potential drug targets, expedites drug development, and aids in biomarker discovery and drug repurposing.

A simplified drug discovery knowledge graph

Consider the application of knowledge graphs in biomarker discovery for Parkinson’s disease. In this instance, the knowledge graph links genetic markers, neural pathways, and clinical observations specific to Parkinson’s disease. By interconnecting these elements, the graph aids researchers in identifying potential biomarkers associated with the disease’s progression, contributing to better diagnostic and therapeutic approaches for Parkinson’s patients.

Breaking down pathways to decipher disease progression

Understanding disease progressions involves complex interactions and pathways. Knowledge graphs can unravel these intricate relationships between molecular entities, genetic pathways, and disease mechanisms, providing a profound comprehension of disease foundations. This is instrumental in precision medicine, biomarker identification, and targeted therapy development within life sciences.

A sample illustration of a knowledge graph that connects knowledge, data, and facts about cancer

Consider the example of breast cancer progression. A knowledge graph, interlinking genetic mutations, cellular pathways, and disease mechanisms, can assist researchers in understanding the intricate relationships between specific genetic markers and the progression of breast cancer. This comprehensive understanding aids in identifying potential therapeutic targets or biomarkers for precise diagnosis and personalized treatment strategies.

Wisecube’s Knowledge Graph Services For the Life Science Industry

Semantically understandable knowledge graphs are invaluable in life sciences, offering access to intricate interdependencies and fostering innovation. As life sciences data expands, knowledge graphs, as data interpreters and integrators, promise a more interconnected, knowledge-driven future in the industry. Wisecube’s Knowledge Graph Engine embodies this vision, propelling the life sciences industry forward with its comprehensive and tailored solutions.

Wisecube is an advanced open-source Knowledge Graph Engine designed to address the complexities arising from the exponential growth of life sciences data. Its AI-powered, biomedical knowledge graph, Orpheus, is meticulously crafted to meet the intricate demands of the life sciences industry. It unifies and synthesizes public and private biomedical data from various sources, encompassing literature, chemical, protein, and side effects databases, and offers customization with proprietary sources.

Wisecube’s knowledge graph services include:

Biomedical entities and their relations in Wisecube’s Orpheus Knowledge Graph (Source: Wisecube)

  • Advanced AI and NLP techniques to extract relationships from complex research articles to derive contextually relevant connections crucial for comprehensive biomedical insights.
  • Unified repository of billions of facts on millions of biomedical entities, forming the backbone of the intricate web of relationships and insights in the life sciences domain.
  • Revelation of hidden connections between genes, diseases, drugs, targets, functions, and toxicological processes, providing rich context for a deeper understanding of interconnectedness within the life sciences industry.
  • User-friendly interface for in-depth exploration, allowing users to seamlessly navigate the extensive repository of information in the life sciences field.

Ready to explore the power of knowledge graphs in life sciences? Connect with us today to learn more about their impact and potential in the industry.

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