Protein subcellular localization prediction with associative classification and multi-class SVM
Bibliographic record
Abstract
Protein subcellular localization prediction is the problem of predicting where a protein functions within a living cell. In this paper, we apply associative classifications (CMAR and CPAR) and multi-class Support Vector Machines to tackle the problem of protein subcellular localization prediction. We use classification feature sources generated from a protein's SwissProt annotation record. We visualize the applied classification rules in an explain graph for domain experts to interpret. We compare the performance of our approaches to those of Proteome Analyst 3.0, using the same set of classification features; we find that all three classification algorithms outperform Proteome Analyst. Multi-class SVM achieves overall F-measures [0.934 ~ 0.991], while CPAR and CMAR achieve overall F-measures [0.922 ~ 0.989] and [0.880 ~ 0.989], respectively. Our result shows that despite multi-class SVM is still the most accurate prediction algorithm with overall F-measures, CPAR and CMAR achieve very similar accuracy. In most cases, CPAR outperforms CMAR, especially when the feature space is large. Our result indicates that associative classification algorithms, especially CPAR, is a good alternative to SVM with similar accuracy but much better transparency in classification models.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".