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
Intrinsically disordered proteins (IDPs) are either entirely disordered or contain disordered regions in their native state. IDPs were found to be abundant in complex organisms and implicated in numerous cellular processes. Experimental annotation of disorder lags behind the rapidly growing sizes of the protein databases, and thus computational methods are used to close this gap and to investigate the disorder. MFDp2 is a novel content-rich and user-friendly web server for sequence-based prediction of protein disorder that builds upon our residue-level disorder predictor MFDp and chain-level disorder content predictor DisCon. It applies novel post-processing filters and uses sequence alignment to improve predictive quality. Using a new benchmark data set, which has reduced sequence identity to corresponding training data sets, MFDp2 is shown to provide competitive predictive quality when compared with MFDp and a comprehensive set of 13 other state-of-the-art predictors, including publicly available versions of the top predictors from CASP9. Our server obtains the highest Mathews Correlation Coefficient (MCC) and the second best Area Under the receiver operating characteristic Curve (AUC). In addition to the disorder predictions, our server also outputs well-described sequence-derived information that allows profiling the predicted disorder. We conveniently visualize sequence conservation, predicted secondary structure, relative solvent accessibility and alignments to chains with annotated disorder. We allow predictions for multiple proteins at the same time and each prediction can be downloaded as text-based (parsable) file. The web server, which includes help pages and tutorial, is freely available at biomine.ece.ualberta.ca/MFDp2/.
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