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Semantic technologies can help streamline the drug development process and increase the chance of success. Here we demystify the various terms and explain how you can tackle the hurdles of getting started.
One of the critical bottlenecks in the drug discovery and development process is the inability to effectively utilize existing knowledge and data to inform decision-making. This can lead to costly delays which are crucial to avoid in a field with such a high failure rate already.
Technology in the life sciences industry is evolving at breakneck speed. Accompanying these revolutions are vast amounts of heterogeneous data. Traditional data management systems struggle to capture and represent the complexities of this data and its relationships adequately, especially since the data comes in many formats and is often siloed. The sheer volume of data and lack of standardization often hinders its effective analysis.
The ability to effectively manage and reuse existing knowledge is crucial for innovation in the pharmaceutical industry.
Semantics is a branch of linguistics and logic regarding meaning and how words, phrases, and symbols within language convey messages and concepts in different contexts. It is crucial for understanding human language. Take, for example, the word "significant." In everyday life it simply means something impactful, whereas in the scientific world it refers to a specific statistical term.
Semantic technology builds on this concept to organize knowledge in a structured, interoperable format that enhances discoverability and facilitates the reuse of information across projects and teams. It plays a pivotal role in managing and interpreting the complex and heterogeneous data generated by drug development activities. In this way, semantic technology can enhance the discovery of relevant information and insights by enabling advanced search capabilities and data analysis across a broad range of data sources.
There is not one single “semantic technology”. This term refers to a wide variety of tools and technologies dealing with meaning that have different focuses: structure, text, intelligence, and so on. Ontologies, knowledge graphs, NLP, and LLMs are all tools and technologies that fall within this domain.
Within semantic technology, an ontology is a formal representation of knowledge within a particular domain. The knowledge is generally grouped into three categories:
Ontologies promote consistency of preferred terms within the field, thereby allowing content to be indexed and retrieved via browsing or searching.
Consolidated data from a range of sources can be represented in a unified knowledge graph, mainly represented through nodes, or points in which pathways intersect or branch. These nodes are connected via edges which allow relationships to be established between them, like linking drug-target interactions.
Further depth and context can be added by enriching the nodes and edges with attributes. For example, a person’s name, age, or occupation could be added to their corresponding node, or an edge annotation could describe relationships such as “employee of”, etc.
Knowledge graphs, therefore, provide more depth to the data than ontologies, and are more useful for complex querying and analytical tasks, whereas ontologies are more useful for obtaining straightforward information.
Natural language processing, or NLP, enables computers to understand, interpret, and generate human language in a meaningful and useful way. It combines a variety of techniques such as speech recognition, language translation, sentiment analysis, and chatbot development. Through analyzing the structure and meaning of words and sentences, NLP can extract insights, identify patterns, and respond to queries in a way that human users can understand.
NLP techniques enable the extraction of structured information from unstructured text, which can then be integrated into a knowledge graph. This combination allows the knowledge graph to be expanded with information from a far wider range of text sources, such as scientific literature and web content.
Ultimately, using NLP in combination with knowledge graphs greatly improves the accessibility and usability of information while also facilitating semantic search, information retrieval, and automated reasoning. These factors are of great benefit to life sciences companies, providing deeper insights and a more informed decision-making process.
Large language models, or LLMs, while not being a true form of semantic technology, are an advanced application of machine learning and NLP that have significant implications for semantic analysis. LLMs analyze vast amounts of data to recognize the statistical patterns and relationships within. In this way, LLMs can enhance the capabilities of semantic technologies, particularly for knowledge graphs.
The combination of LLMs with knowledge graphs provides huge value to pharmaceutical organizations.
The semantic technologies described above combine to create powerful tools for data analysis and insight discovery for drug development and beyond. These tools can offer huge potential benefits for life sciences companies, some of which are described below.
Semantic technologies facilitate the integration of disparate data sources, such as genomic data, clinical trial data, and research publications, to provide a comprehensive view of relevant information. This can be leveraged to accelerate the drug discovery process by identifying potential drug targets, enhancing understanding of disease mechanisms, and predicting drug efficacy and side effects.
When setting up clinical trials, many factors need to be balanced, including ethical considerations, patient safety, regulatory requirements, and practical feasibility. Semantic technologies can improve the design and execution of clinical trials by helping to identify suitable trial sites and trial candidates through analyzing relevant data sources. They can also support the monitoring of trial progress and outcomes by aggregating and analyzing results from various sources.
Pharmaceutical companies must navigate a complex regulatory landscape, ensuring compliance with standards and regulations across different regions and stages of drug development. Semantic technologies can streamline the management and analysis of regulatory information, improving the efficiency and accuracy of compliance processes and reporting.
Vast amounts of knowledge are generated and stored in various formats by the pharmaceutical industry. Here, semantic technologies can enhance knowledge management practices by enabling the organization, retrieval, and synthesis of information, facilitating innovation and collaboration within and across organizations.
Leveraging semantic technology can allow your organization to harness the full potential of your data sets. However, implementing this technology can be a daunting task. During a recent webinar we learned that over 60% of the audience identified a lack of understanding of the technology or a lack of organizational readiness as the main obstacles to implementing semantic technology within their organization.
To respond to these uncertainties, we have addressed the two major concerns in our latest e-book “Linking data to unlock insights: the building blocks of semantic technology”. Learn more about semantic technologies, and dive into their origins and how they work. We also lay out guidelines on how to facilitate their integration into your company, whatever its size, to help you achieve success.
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