Models and data of AMPlify: a deep learning tool for antimicrobial peptide prediction
Bibliographic record
Abstract
OBJECTIVES: Antibiotic resistance is a rising global threat to human health and is prompting researchers to seek effective alternatives to conventional antibiotics, which include antimicrobial peptides (AMPs). Recently, we have reported AMPlify, an attentive deep learning model for predicting AMPs in databases of peptide sequences. In our tests, AMPlify outperformed the state-of-the-art. We have illustrated its use on data describing the American bullfrog (Rana [Lithobates] catesbeiana) genome. Here we present the model files and training/test data sets we used in that study. The original model (the balanced model) was trained on a balanced set of AMP and non-AMP sequences curated from public databases. In this data note, we additionally provide a model trained on an imbalanced set, in which non-AMP sequences far outnumber AMP sequences. We note that the balanced and imbalanced models would serve different use cases, and both would serve the research community, facilitating the discovery and development of novel AMPs. DATA DESCRIPTION: This data note provides two sets of models, as well as two AMP and four non-AMP sequence sets for training and testing the balanced and imbalanced models. Each model set includes five single sub-models that form an ensemble model. The first model set corresponds to the original model trained on a balanced training set that has been described in the original AMPlify manuscript, while the second model set was trained on an imbalanced training set.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".