Unsupervised Whole-Genome Representation Learning Captures Bacterial Phenotypes
Bibliographic record
Abstract
Abstract Shifting from hand-crafted to learned representations of data has revolutionized fields like natural language processing and computer vision. Despite this, current approaches to bacterial phenotype prediction from the genome rely on training machine learning models on hand-crafted features, often binary indicators or counts of the presence of different conserved genomic elements and protein domains. Defining these shared elements and domains as our “genomic element vocabulary”, we tokenize entire bacterial genomes as sequences of these conserved elements and take advantage of advances in long-context language modeling to perform self-supervised whole-genome representation learning (WGRL). Through multi-task pretraining on a phylogenetically diverse dataset of hundreds of thousands of bacterial genomes, we present a genomic language model which produces representations of input genomes with features predictive of a broad range of phenotypes. We assess the quality of the learned representations through k-nearest neighbours prediction of 25 bacterial phenotypes, finding our WGRL representations more predictive than standard protein domain presence/absence representations for 23/25 different phenotypes. We additionally find the WGRL representations are robust to both poor genome assembly quality and incompleteness. Through learning the relationships between evolutionarily conserved genomic elements with self-supervised long-context language modeling, we demonstrate the first approach for extracting general-purpose whole-genome representations while preserving gene order.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".