ConPred II: a consensus prediction method for obtaining transmembrane topology models with high reliability
Bibliographic record
Abstract
ConPred II (http://bioinfo.si.hirosaki-u.ac.jp/~ConPred2/) is a server for the prediction of transmembrane (TM) topology [i.e. the number of TM segments (TMSs), TMS positions and N-tail location] based on a consensus approach by combining the results of several proposed methods. The ConPred II system is constructed from ConPred_elite and ConPred_all (previously named ConPred), proposed earlier by our group. The prediction accuracy of ConPred_elite is almost 100%, which is achieved by sacrificing the prediction coverage (20-30%). ConPred_all predicts TM topologies for all the input sequences with accuracies improved by up to 11% over individual proposed methods. In the ConPred II system, the TM topology prediction of input TM protein sequences is executed following a two-step process: (i) input sequences are first run through the ConPred_elite program; (ii) sequences for which ConPred_elite does not give the TM topology are delivered to the ConPred_all program for TM topology prediction. Users can get access to the ConPred II system automatically by submitting sequences to the server. The ConPred II server will return the predicted TM topology models and graphical representations of their contents (hydropathy plots, helical wheel diagrams of predicted TMSs and snake-like diagrams).
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.013 |
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".