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 Uniform Manifold Approximation and Projection 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=.217). Term-name length increased with depth (Pearson r = .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).

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BibTeX RIS

BibTeX

@article{ahmad2026letters,
  title = {{Letters and biological process: linguistic structure encodes hierarchical depth in the gene ontology}},
  author = {Sadiya Ahmad},
  journal = {Bioinformatics},
  year = {2026},
  month = oct,
  doi = {10.1093/bioinformatics/btag768},
  url = {https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag768/8913739},
  note = {CC BY 4.0},
}

RIS

TY  - JOUR
TI  - Letters and biological process: linguistic structure encodes hierarchical depth in the gene ontology
AU  - Sadiya Ahmad
PY  - 2026
DA  - 2026/10/13
JO  - Bioinformatics
DO  - 10.1093/bioinformatics/btag768
UR  - https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag768/8913739
AB  - 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 Uniform Manifold Approximation and Projection visualization and k-means clustering (k=20) recovered biologically coherent semantic groups whose depth distributions differed strongly (Kruskal–Wallis H=5244; p&lt;10−300; η2=.217). Term-name length increased with depth (Pearson r = .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).
SN  - 1367-4803
SN  - 1367-4811
ER  -

Abstract

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