Bibliographic record
Abstract
The end of last century witnessed the booming of bioinformatics. Bioinformatics is now becoming one of the most active research fields. One of the major problems facing bioinformatians is how to align two or more biological sequences. The question of multiple sequence alignment has been studied for a long time, may algorithms have been proposed. One solution is to reduce the multiple sequence alignment to the problem of aligning two alignments. Whether the latter question is NP-complete or not is an open problem. In this thesis, we will prove that it is NP-complete, thus this open problem is solved. In addition, we will propose a fast approximation algorithm to solve the problem of aligning two alignments question. Aligning two RNA sequences could reveal the relatedness of them. However, during long time of revolution, the RNA sequences may have changed greatly, yet their structures may preserve the same shape. Thus aligning two RNA structures may exhibit more accurate relationship. Traditionally, an RNA secondary structure is modeled as a tree which is not suitable to align multiple RNA structures. In this thesis, we will propose a new model to align RNA structures which can also be applied to align RNA tertiary structures. We will also propose a series of algorithms to align pairwise RNA structures and multiple RNA structures which are based upon the new model.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".