Parallel Optimal Pairwise Biological Sequence Comparison
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many bioinformatics applications, such as the optimal pairwise biological sequence comparison, demand a great quantity of computing resource, thus are excellent candidates to run in high-performance computing (HPC) platforms. In the last two decades, a large number of HPC-based solutions were proposed for this problem that run in different platforms, targeting different types of comparisons with slightly different algorithms and making the comparative analysis of these approaches very difficult. This article proposes a classification of parallel optimal pairwise sequence comparison solutions, in order to highlight their main characteristics in a unified way. We then discuss several HPC-based solutions, including clusters of multicores and accelerators such as Cell Broadband Engines (CellBEs), Field-Programmable Gate Arrays (FPGAs), Graphics Processing Units (GPUs) and Intel Xeon Phi, as well as hybrid solutions, which combine two or more platforms, providing the actual landscape of the main proposals in this area. Finally, we present open questions and perspectives in this research field.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it