An Explainable Deep Few-shot Network for Protein Family Classification
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
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.
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 | 0.000 |
| 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