Leveraging Large Language Models for Automatic Hypotheses Testing over Heterogeneous Biological Databases

An understanding of the molecular basis of musculoskeletal pain is necessary for the development of therapeutics, their management, and possible personalization. One-in-three Americans use OTC pain killers, and one tenth use prescription drugs to manage pain. The CDC also estimates that about 20% Americans suffer from chronic pain. As the experience of acute or chronic pain varies due to individual genetics and physiology, it is imperative that researchers continue to find novel therapeutics to treat or manage symptoms. In this paper, our goal is to develop a seed knowledgebase computational platform, called BioNursery, that will allow biologists to computationally hypothesize, define and test molecular mechanisms underlying pain. In our knowledge ecosystem, we accumulate curated information from users about the relationships among biological databases, analysis tools, and database contents to generate biological analyses modules, called π-graphs, or process graphs. We propose a mapping function from a natural language description of a hypothesized molecular model to a computational workflow for testing in BioNursery. We use a crowd computing feedback and curation system, called Explorer, to improve proposed computational models for molecular mechanism discovery, and growing the knowledge ecosystem.