Gene function prediction using an AnnoTree-based genomic language model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Large tree-of-life (ToL) scale databases of microbial genomes are powerful resources for exploring genome structure and function from a phylogenomic context. Despite the growing availability of genomic data, large-scale genome annotation is still a challenge, with a considerable fraction of genes remaining as unannotated. Here, to expand the capabilities of database-wide gene function prediction, we used our AnnoTree platform as a corpus for training a Word2vec-based genomic language model (gLM). Machine-learning of genomic grammar patterns across the AnnoTree database revealed functional associations between genes and enabled the inference of function for hypothetical proteins and domains, as we demonstrated by predicting novel type VI secretion proteins. Finally, we implemented a web-server to allow users to interact with the AnnoTree Word2vec model, thus facilitating gene function prediction. Ultimately, our work highlighted the GTDB/AnnoTree database as a powerful training database for gLMs focused on prediction and discovery of microbial gene functions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it