Learned Random-Walk Kernels and Empirical-Map Kernels for Protein Sequence Classification
Bibliographic record
Abstract
Biological sequence classification (such as protein remote homology detection) solely based on sequence data is an important problem in computational biology, especially in the current genomics era, when large amount of sequence data are becoming available. Support vector machines (SVMs) based on mismatch string kernels were previously applied to solve this problem, achieving reasonable success. However, they still perform poorly on difficult protein families. In this paper, we propose two approaches to solve the protein remote homology detection problem: one uses a convex combination of random-walk kernels to approximate the random-walk kernel with the optimal random steps, and the other constructs an empirical-map kernel using a profile kernel. Both resulting kernels make use of a large number of pairwise sequence similarity information and unlabeled data; and have much better prediction performance than the best profile kernel directly derived from protein sequences. On a competitive Structural Classification Of Proteins (SCOP) benchmark dataset, the overall mean ROC(50) scores on 54 protein families we obtained using both approaches are above 0.90, which significantly outperform previous published results.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".