An Explainable Deep Few-shot Network for Protein Family Classification
Bibliographic record
Abstract
Protein sequence analysis is arguably a challenging bioinformatics problem covering various areas and applications such as sequence annotation, metagenomics, and comparative genomics.Recent proteomics studies report the superior results of machine learning techniques in comparison to conventional alignment-based and alignment-free methods for analyzing protein sequences.However, the machine learning techniques are dependent on handcrafted features, often extracted from large-scale data sets, that may require domain knowledge in addition to analytics expertise.In this study, by leveraging a deep language model, designed for proteins, and transfer learning, we propose an explainable high-performing deep few-shot Siamese network for the protein family classification task.To the best of our knowledge, this is the first explainable deep network tailored for primary sequence family classification that can highly perform with a very limited number of observations.We are now running intensive experiments, both quantitatively and clinically, to validate the proposed network.We plan to release the network and findings publicly once the validation process is terminated.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".