Guiding vaccine development with machine learning
From tackling homework challenges to drafting emails, people are discovering a vast array of applications for natural language processing tools like generative artificial intelligence (AI) engines. Now, researchers from Pacific Northwest National Laboratory (PNNL) and Harvard Medical School (HMS) are using this same kind of technology to build a knowledge base in order to guide decision-makers on vaccine development. Through the Rapid Assessment of Platform Technologies to Expedite Response (RAPTER) project, the scientists leverage machine learning and AI to search the scientific literature for knowledge on how to build effective vaccines against new infectious viruses and bacteria.
With RAPTER, researchers figure out which strategy would work best for a specific virus or bacteria to maximize the value of immune responses from the host. The tool aims to help produce new vaccines more rapidly and with a reduced timeline and cost.
Under the RAPTER project, PNNL scientists work closely with colleagues from HMS to automatically extract information from scientific publications in a meaningful way.
PNNL and HMS scientists make this possible by defining the key terms that connect mechanisms of immunity to experimental measurements. Once the terms are defined, the RAPTER tool can identify the relationships between terms across different scientific publications. This information feeds into the Knowledge Extraction for Strategic Threat Response using Evidence from the Literature (KESTREL) database to build an extensive graph of relationships in the immune response.
