RefEx, a reference gene expression dataset as a web tool for the functional analysis of genes
Bibliographic record
Abstract
Gene expression data are exponentially accumulating; thus, the functional annotation of such sequence data from metadata is urgently required. However, life scientists have difficulty utilizing the available data due to its sheer magnitude and complicated access. We have developed a web tool for browsing reference gene expression pattern of mammalian tissues and cell lines measured using different methods, which should facilitate the reuse of the precious data archived in several public databases. The web tool is called Reference Expression dataset (RefEx), and RefEx allows users to search by the gene name, various types of IDs, chromosomal regions in genetic maps, gene family based on InterPro, gene expression patterns, or biological categories based on Gene Ontology. RefEx also provides information about genes with tissue-specific expression, and the relative gene expression values are shown as choropleth maps on 3D human body images from BodyParts3D. Combined with the newly incorporated Functional Annotation of Mammals (FANTOM) dataset, RefEx provides insight regarding the functional interpretation of unfamiliar genes. RefEx is publicly available at http://refex.dbcls.jp/.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.012 |
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".