A flexible search system for high-accuracy identification of biological entities and molecules
Bibliographic record
Abstract
Identifying subcellular biological entities (genes, gene products, and small molecules) is essential in using and creating bioinformatics analysis tools, text mining, and accessible biological research apps.When research information is uniquely and unambiguously identified, it enables data to be accurately retrieved, cross-referenced, and integrated.In practice, biological entities are identified when they are associated with a matching record from a knowledge base that specialises in collecting and organising information of that type (e.g.genes in NCBI Gene).Our search service increases the efficiency and ease of use for identifying biological entities compared to prior approaches (Berriz & Roth, 2008;Côté et al., 2007;Juty et al., 2012;Reimand et al., 2007).A comparison of similar tools is available in the software documentation (Franz et al., 2021a).This identification service may be used to power research apps and tools, such as Biofactoid, GeneMANIA, and STRING, where colloquial entity names may be provided as input by users (Mering et al., 2003;
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.018 |
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".