Bibliographic record
Abstract
There has been a massive increase in the number of large scale biological datasets during the past twenty years, producing new challenges and complexities for analysis. Many of these new datasets are in the ‘omics fields, involving analysis of the genome, transcriptome, and proteome among others. Here, we review ‘omics community-specific factors affecting use of bioinformatics workflow systems. We identify the characteristics of the audience for scientific workflow systems in this community, the existence of a large amount of prewritten software, the use of large amounts of data in a typical analysis, and the growing complexity of analyses as important factors in considering workflow design criteria in this field and also future development of Kepler. Generally, many factors favor much increased use of Kepler in bioinformatics in the future, in particular its advantages in comprehensibility, extensibility, and modifiability of bioinformatics pipelines. We suggest concrete steps to enable further use of this flexible workflow system in ‘omics analyses.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.121 | 0.158 |
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".