TooT-BERT-M: Discriminating Membrane Proteins from Non-Membrane Proteins using a BERT Representation of Protein Primary Sequences
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Bibliographic record
Abstract
Membrane proteins account for approximately 30% of all proteins in a cell. These proteins are difficult to study due to their hydrophobic surface and reliance on the original in vivo environment. This paper proposes TooT-BERT-M, a technique for detecting membrane proteins based on the Bidirectional Encoder Representations from Transformers (BERT) representation. BERT is a technique for learning contextual embeddings for individual amino acids in a protein sequence. We trained a Logistic Regression to discriminate membrane proteins from non-membrane proteins and obtained a classification accuracy of 92.46% and an MCC of 0.85. Additionally, we compared frozen and fine-tuned BERT representations to determine which should be used for membrane protein identification. The results are cutting-edge in predicting membrane proteins and statistically significant compared to previous work.
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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 | 0.001 |
| 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 it