Analysis of hot regions prediction in PPI with different amino acid mutation using machine learning algorithm
Bibliographic record
Abstract
Discovering hot regions in protein-protein interaction is important for drug and protein design, while experimental identification of hot regions is a time-consuming and labor-intensive effort, meanwhile, different amino acid mutations will bring different energy changes, which lead to different features selection in predictive models; thus, the analysis of predictive models with different amino acid mutation using machine learning algorithm can be very helpful. In this paper, firstly 20 datasets are obtained according to all 20 kinds of amino acid using mutation data from the SKEMPI; then predictive models by combining feature-based classification and density-based incremental clustering were applied in datasets separately. Experiment results show that RctASA, Hydrophobicity, BpASA and BminCX are the best features to discriminate hot spots and non-hot spots at all the datasets according to different kinds of amino acid mutations, and the dataset with LEU, GLY and PRO mutations have the better prediction performance than the other amino acid mutations. These analyses help us to achieve a better insight on protein and their interactions.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".