Learning Random-Walk Kernels for Protein Remote Homology Identification and Motif Discovery
Bibliographic record
Abstract
Random-walk based algorithms are good choices for solving many classification problems with limited labeled data and a large amount of unlabeled data. However, it is difficult to choose the optimal number of random steps, and the results are very sensitive to the parameter chosen. In this paper, we will discuss how to better identify protein remote homology than any other algorithm using a learned random-walk kernel based on a positive linear combination of random-walk kernels with different random steps, which leads to a convex combination of kernels. The resulting kernel has much better prediction performance than the state-of-the-art profile kernel for protein remote homology identification. On the SCOP benchmark dataset, the overall mean ROC50 score on 54 protein families we obtained using the new kernel is above 0.90, which has almost perfect prediction performance on most of the 54 families and has significant improvement over the best published result; moreover, our approach based on learned random-walk kernels can effectively identify meaningful protein sequence motifs that are responsible for discriminating the memberships of protein sequences' remote homology in SCOP.
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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 | 0.007 |
| 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 | 0.002 |
| 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".