Using sequence-to-sequence LSTM to predict RNA virus mutations
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
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.
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 | 0.001 |
| 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