Gene Ontology Tools: A Comparative Study
Bibliographic record
Abstract
Gene ontology (GO) is a key initiative of bioinformatics to provide uniform description of gene product in different public databases. The GO project has initiated 3 (three) structured and organized controlled vocabularies (i.e. ontology) that describes gene products in term of cellular components, biological processes and functions in an independent of species. This resource saves lots of time and effort in finding information of any particular gene from different databases. Since 1998, many tools have been developed which at one end relieves the researchers to search particulars about gene products but at other end makes difficult to opt most appropriate tool for any certain investigation in gene ontology. Here we present the state-of-the-art web based GO tools currently used for biological ontologies. We adopt comparison methodology in conjunction with visualization capabilities and sources of annotation data. This paper considers three GO tools for the said purpose. In Visualization capabilities; Indented List, Node-link & tree and Zoomable capabilities of selected tools have been analyzed. In the data sources section; currently available sources for data annotation have been discussed. This review will facilitate potential users of the GO tools to select an appropriate tool for their need.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".