Letters and Biological Process: Linguistic Structure Encodes Hierarchical Depth in the Gene Ontology.

Abstract

Motivation: We asked whether the natural language used to name Gene Ontology (GO) Biological Process terms encodes hierarchical position in the GO directed acyclic graph.

Results: Semantic representations of term names, combined with UMAP visualization and k-means clustering (k=20) recovered biologically coherent semantic groups whose depth distributions differed strongly (Kruskal-Wallis H=5244, p<10-300; η2=0.217). Term-name length increased with depth (Pearson r=0.34, Spearman ρ=0.37; slope ≈3.2 letters per level), and this positive scaling held under shortest-, longest-, and average-path depth definitions. Vocabulary entropy followed a bell-shaped diversification-specialization curve with an intermediate-depth peak (maximal at depth 4) that lies outside a Monte Carlo null envelope across most depths, and null-model tests supported a genuine linguistic-hierarchical association.

Availability and implementation: The go_lingdepth Python package, regenerated result tables, and figure-generation scripts are available at https://github.com/Sadiya-ISU/Letters-and-Biological-Process and archived on Zenodo (DOI: 10.5281/zenodo.21501473).

Supplementary information: Supplementary data are available at Bioinformatics online.