Protein folding rate prediction integrating multi-level structural information
Bibliographic record
Abstract
Studying protein folding can not only drive the great development of life sciences, but also provide tremendous help for human disease prevention and treatment, and has great application value in the fields of medicine and bioengineering. This article uses the BP neural network model to predict the rate of protein folding, and provides an effective way to find the key factors of protein folding kinetics. The main research contents are as follows: (1) Optimization of the neural network model. This article selected 4 types of optimizers and 36 types of activation function combinations to assess the performance of the neural network model in predicting the rate of protein folding. From the results, it is more accurate and fast to predict the rate of protein folding when using the Adam optimizer and Sigmoid and Tanh function combinations as the parameters of the neural network model. (2) The influence of chain length in primary structure information on prediction accuracy is studied and compared. From the prediction results, it was found that the prediction accuracy was higher when using the effective chain length of the protein than using the protein chain length. (3) Study the effect of different separation cutoffs on protein folding rates. The results show that when the separation cutoff value lcut of the contact order is 3, the most accurate prediction value can be obtained.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".