BACTIBASE: a new web-accessible database for bacteriocin characterization
Bibliographic record
Abstract
BACKGROUND: Bacteriocins are very diverse group of antimicrobial peptides produced by a wide range of bacteria and known for their inhibitory activity against various human and animal pathogens. Although many bacteriocins are now well characterized, much information is still missing or is unavailable to potential users. The assembly of such information in one central resource such as a database would therefore be of great benefit to the exploitation of these bioactive molecules in the present context of increasing antibiotic resistance and natural bio-preservation need. DESCRIPTION: In the present paper, we present the development of a new and original database BACTIBASE that contains calculated or predicted physicochemical properties of 123 bacteriocins produced by both Gram-positive and Gram-negative bacteria. The information in this database is very easy to extract and allows rapid prediction of relationships structure/function and target organisms of these peptides and therefore better exploitation of their biological activity in both the medical and food sectors. CONCLUSION: The BACTIBASE database is freely available at http://bactibase.pfba-lab.org, web-based platform enabling easy retrieval, via various filters, of sets of bacteriocins that will enable detailed analysis of a number of microbiological and physicochemical data.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".