Machine Learning Enables Viral Genome-Agnostic Classification of RNA Virus Infections from Host Transcriptomes
Bibliographic record
Abstract
ABSTRACT Targeted PCR diagnosis of RNA viruses is sequence dependent, meaning that the accuracy of the assay depends on the identity of the viral sequence. However, sequence-targeted assays can miss novel or divergent viruses. We test whether the host transcriptome alone can classify RNA virus infections without using viral sequences. Using publicly available data on Huh7 and Calu-3 cells experimentally infected with diverse negative-sense RNA, we evaluate two host-derived feature sets (differential expression dataset of identified genes and alignment-free nucleotide k-mer spectra) and compare hierarchical clustering with Random Forests. Across datasets and timepoints (12 to 24 hpi), Random forests accurately distinguished cells infected with different viruses. Controls with label permutation and read shuffling across experimental conditions established non-random performance. Influenza A infections exhibited the strongest, most distinct signatures, whereas Ebola and Lassa virus responses were subtler yet still classifiable. These results show that host-only transcriptomics encodes virus-specific, complex signals that machine learning can exploit, enabling genome-agnostic classification of infection statuses. This approach could aid early outbreak triage when primer based detection methods fail or viral genomes are unknown and complements sequence-based discovery.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".