Bibliographic record
Abstract
Infection with viruses is one of the main causes of human illness and even death in today's society. Vaccination can help people fight the virus. However, virus mutation will always cause vaccines to fail. Predicting the mutations of viral could help detect the mutated virus early and develop new vaccines so to reduce the rate of infection and death from the virus. The common strategy used to predict virus mutation is determining the important components of the virus, like amino acids and proteins, then using deep learning or machine learning methods to construct models. It is an effective strategy. However, using this strategy always cause people to spend lots of time and money studying the component of the virus, like amino acids, proteins, nucleic acid and so on. To increase efficiency and reduce the cost of research, a new method that can predict virus mutation based on nucleotide sequence alone is something people looking forward to. In recent years, in natural language processing, building a sequence-to-sequence model is becoming a popular and effective method to deal with textual data. As a type of text data, using the idea of sequence to sequence should be good to deal with the RNA sequence. In addition, in terms of the past study of predicting virus mutation, RNA sequence as a kind of time sequence, people generally use long short-term memory (LSTM) methods to handle it. Thus, in this study, we would combine the idea of the sequence-to-sequence model with LSTM method to predict the possible mutation of the virus. This experiment studies the mutation of two typical influenza viruses and achieves encouraging results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".