mycoCLAP, the database for characterized lignocellulose-active proteins of fungal origin: resource and text mining curation support
Bibliographic record
Abstract
Enzymes active on components of lignocellulosic biomass are used for industrial applications ranging from food processing to biofuels production. These include a diverse array of glycoside hydrolases, carbohydrate esterases, polysaccharide lyases and oxidoreductases. Fungi are prolific producers of these enzymes, spurring fungal genome sequencing efforts to identify and catalogue the genes that encode them. To facilitate the functional annotation of these genes, biochemical data on over 800 fungal lignocellulose-degrading enzymes have been collected from the literature and organized into the searchable database, mycoCLAP (http://mycoclap.fungalgenomics.ca). First implemented in 2011, and updated as described here, mycoCLAP is capable of ranking search results according to closest biochemically characterized homologues: this improves the quality of the annotation, and significantly decreases the time required to annotate novel sequences. The database is freely available to the scientific community, as are the open source applications based on natural language processing developed to support the manual curation of mycoCLAP. Database URL: http://mycoclap.fungalgenomics.ca.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.026 |
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".