Genome Homology Visualization by Short Similar Substring Enumeration (Acceleration and Visualization of Computation for Enumeration Problems)
Bibliographic record
Abstract
Finding similar substrings/substructures is a central task in analyzing huge amounts of genome data.In the sense of complexity theory, the existence of polynomial time algorithms for such problems is usually trivial since the number of substrings is bounded by the square of their lengths.However, straightforward algorithms do not work for practical huge databases because of their computation time of high degree order.This paper addresses the problems of finding pairs of strings with small Hamming distances from huge databa.es composed of short strings of a fixed length.Using this, we compare two genomc scqucnces by solving this problcm for all the fixed- length substrings taken from the sequences.We focus on the practical efficiency of algorithms, and propose an algorithm running in almost linear tlme of the database size.When there are so many similar pairs so that the visualization is impossible, we propose to use a filtering algorithm to remove the pairs which are not parts of similar long sequences.Computational experiments for genome sequcnces show the efficiency of thc method.An implementation is available at the author's homepagel
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.005 | 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".