Bibliographic record
Abstract
ABSTRACT Motivation Non-coding RNAs (ncRNAs) play important roles in various biological processes. In past, homologousncRNA search in genomic scale (e.g., search all house mouse ncRNAs for several human ones) is difficult since explicit consideration of secondary structure in alignment leads to impractical complexity on both of time and space. Results In this study, building the program CRAST (Context RNA Alignment Search Tool, available at “ https://github.com/heartsh/crast ” including the used validation/test set), we developed the CRAST algorithm, a “seed-and-extend” alignment one based on adaptive seed and RNA secondary structure context (motif probabilities) as in Fig. 1. The algorithm is O ( n : a sum of lengths of target sequences ) on time through help of adaptive seed, implicitly considering both of sequence and secondary structure; it provides computation time comparable with other BLAST-like tools, significantly reduced from any variant of the Sankoff algorithm for alignment with the explicit consideration. It detects homologs as many as other BLAST-like tools and the lowest number of non-homologous ncRNAs.
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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".