TooT-BERT-C: A study on discriminating ion channels from membrane proteins based on the primary sequence’s contextual representation from BERT models
Bibliographic record
Abstract
While ion channels play a critical role in a variety of physiological processes and are a frequent therapeutic target, their function that contributes to disease remains unknown. Computational techniques have emerged as crucial and indispensable tools for understanding ion channels and their function in recent years. This is because their mechanism of action is complex, and a static representation of an ion channel is frequently insufficient to comprehend the underlying process. This article introduces TooT-BERT-C, a technique that utilizes the BERT contextual representation to assess and discriminate ion channels from membrane proteins via a Logistic Regression classifier. Additionally, we compare two alternative BERT models’ frozen and fine-tuned representations, namely ProtBERT-BFD and MembraneBERT. When compared to leading deep learning prediction algorithms, TooT-BERT-C has the highest accuracy of 98.24 percent and MCC of 0.85.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".