Multiple Anchor Staged Local Sequence Alignment Algorithm - MASAA.
Bibliographic record
Abstract
Technology advancements have helped biologists gather massive amount of biological data including genomic sequences of various species today. Sequence alignment techniques play a central role in investigating the adaptive significance of organism traits and revealing evolutionary relations among organisms by comparing these biological data. This thesis presents an algorithm to perform pairwise local sequence alignment. Recent pairwise local sequence alignment algorithms are either slow and sensitive or fast and less sensitive. Our algorithm is faster and at the same time sensitive. The algorithm employs suffix tree data structure to accurately identify long common subsequences in the two given sequences quickly. Regions of high similarity are again identified between segments of long subsequences already found. Several measures are taken into consideration to design the algorithm, such that the output is biologically meaningful. Data sets are carefully chosen and the output is compared with a well known algorithm, BLASTZ. Experiments conducted demonstrate that our algorithm performs better than BLASTZ in computation time, while either preserving or exceeding the accuracy of alignments at times.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.009 |
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".