Sars-Cov-2 Spike protein function prediction using a convolutional neural network ensemble
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
Introduction: SARS-CoV-2 has become a worldwide pandemic that affects all aspects of life; therefore, numerous organizations and open exploration foundations focus their efforts on research for viable therapeutics. Given past experiences and involvement in SARS, the essential focus has been the Spike protein, considered as the perfect objective for COVID-19 immunotherapies. Most of the vaccines being developed target the spike proteins because this protein covers the virus and helps it invade human cells. Methods: Applications of deep neural network is a quickly expanding field now reaching many areas including proteomics. Results: To be precise, convolutional neural networks have been used for identifying the functional role of amino acid sequences, because of its ability to give nearly accurate results for multi-label classification problems. Here we present a modified convolutional deep learning model that can identify if a given amino acid sequence is a spike protein or not based on the length of the sequence and the function of the protein, that will be done with a short execution time and a relatively small error rate.
 Conclusion: CNN is an efficient tool at supervised multilabel classification problems
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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