  Interrogation of biological pathways is an integral part of omics data
analysis. Large language models (LLMs) enable the generation of custom pathways
and gene sets tailored to specific scientific questions. These targeted sets
are significantly smaller than traditional pathway enrichment analysis
libraries, reducing multiple hypothesis testing and potentially enhancing
statistical power. Lomics (Large Language Models for Omics Studies) v1.0 is a
python-based bioinformatics toolkit that streamlines the generation of pathways
and gene sets for transcriptomic analysis. It operates in three steps: 1)
deriving relevant pathways based on the researcher's scientific question, 2)
generating valid gene sets for each pathway, and 3) outputting the results as
.GMX files. Lomics also provides explanations for pathway selections.
Consistency and accuracy are ensured through iterative processes, JSON format
validation, and HUGO Gene Nomenclature Committee (HGNC) gene symbol
verification. Lomics serves as a foundation for integrating LLMs into omics
research, potentially improving the specificity and efficiency of pathway
analysis.
