A multitask neural network trained on embeddings from ESMFold can accurately rank order clinical outcomes for different cystic fibrosis mutations
Bibliographic record
Abstract
Advancements in AI-based protein structure prediction have opened new research avenues in computational biology. Previous work has shown mixed results when the predicted 3D structure or model embeddings have been used as features for predicting phenotypes. Many existing tools which predict the pathogenicity of variants (e.g. SIFT 4G, Polyphen-2, REVEL, LYRUS) have incorporated structural protein features, but have shown mixed success when applied to mutations on the CFTR gene. This paper explores whether embeddings from the ESM-Fold model, which predicts the 3D structure of a protein from an amino acid sequence, can be used to predict clinical outcomes for different Cystic Fibrosis (CF) genotypes found in the CFTR2 database. A neural network model trained on these embeddings is able to obtain both non-trivial and statistically significant correlations on CF-related phenotypes (sweat chloride, pancreatic insufficiency, and infection rates). Overall, AI-based protein structure prediction models show a promising ability to assess the relative severity of CFTR-based mutations on key phenotypic outcomes associated with CF. The entire processing and analysis pipeline for this work can be found at this repo .
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".