Research and Development (R&D) teams in the life sciences sector generate a diverse range of data and data types, including pre-clinical assay results, industry analyses, clinical trial data, and more. Ideally, this wealth of information should be analyzed comprehensively to inform future discoveries and strategies. However, the sheer breadth and volume of data often lead to siloed information and fragmented decision-making processes within organizations. This fragmentation hampers the ability to harness the full potential of available data for making data-driven decisions.
To unify fragmented knowledge in life sciences, we introduced a solution that connects a biologically relevant Large Language Model to diverse sources of biomedical data, including both structured and unstructured data.
We use the concepts behind LLMs to vectorize all relevant information, drawing connections between diverse sources. These knowledge vectors allow us to index and prioritize, creating a search engine for biologic data. This approach allows the LLM to “crawl” over all data, producing responses and surfacing information to create a holistic narrative by pulling on threads of insight across all underlying data.
This solution unifies diverse datasets, formats, and data types without needing extensive work to format and unify all into a single database. This approach can streamline R&D knowledge management, break data silos and foster data-driven decision-making, empowering life science organizations to accelerate discovery and innovation.