APDeeM: A machine Learning strategy towards Effective Peptide Vaccine Candidates Identification against Different Types of Viruses
Bibliographic record
Abstract
Abstract Viral infections pose significant global health challenges, underscoring the urgent need for improved medications. Nevertheless, traditional medicinal approaches depend significantly on labor-intensive laboratory tests, which impede efficient identification and prolong vaccine development, particularly when screening a huge number of samples. To address these obstacles, we present a comprehensive Antiviral Peptide (AVP) Detection Dataset, comprising 14 unique features to improve the characterization of antiviral and non-antiviral peptides. Subsequently, we introduce the Antiviral Peptide detection enhanced by Ensemble Machine Learning (APDeeM) system. This advanced computational framework considerably reduces the time required for AVP detection by utilizing ensemble learning methodologies. The APDeeM system incorporates Gradient Boosting, Random Forest, K-Nearest Neighbors (KNN), and AdaBoost algorithms to facilitate the swift selection of AVP candidates without requiring urgent laboratory testing. Our proposed ensemble methodology showed superior performance, with an accuracy of 85.99%, F1 score of 87.60%, recall of 88.91%, and precision of 86.32%, exceeding the efficacy of all tested antiviral peptide prediction models in this research. The APDeeM approach signifies a substantial improvement over conventional detection techniques, expediting the identification of prospective vaccine candidates and facilitating the advancement of more effective antiviral peptides. The most promising AVP candidates may urge laboratory validation, optimize resources, and accelerate vaccine development.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".