Computing Maximal Covers for Protein Sequences
Bibliographic record
Abstract
A partial cover of a string or sequence of length n, which we model as an array , is a repeating substring u of x such that “many” positions in x lie within occurrences of u. A maximal cover u*—introduced in 2018 by Mhaskar and Smyth as optimal cover—is a partial cover that, over all partial covers u, maximizes the positions covered. Applying data structures also introduced by Mhaskar and Smyth, our software MAXCOVER for the first time enables efficient computation of u* for any x—in particular, as described here, for protein sequences of Arabidopsis, Caenorhabditis elegans, Drosophila melanogaster, and humans. In this protein context, we also compare an extended version of MAXCOVER with existing software (MUMmer's repeat-match) for the closely related task of computing non-extendible repeating substrings (a.k.a. maximal repeats). In practice, MAXCOVER is an order-of-magnitude faster than MUMmer, with much lower space requirements, while producing more compact output that, nevertheless, yields a more exact and user-friendly specification of the repeats.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".