ProbeLynx: a tool for updating the association of microarray probes to genes
Bibliographic record
Abstract
As genome sequence data and gene prediction improve, probes developed for a given microarray experiment should be continuously re-evaluated for their specificity for given genes. ProbeLynx(www.pathogenomics.ca/probelynx) is a new web service which uses current genomic sequence information to re-examine microarray probe specificity and provide annotation updates relevant to determining which gene(s) and transcript(s) are associated with a given probe. Probe sequences (either oligonucleotide- or cDNA-based) are uploaded in FASTA format and the results returned as a tab-delimited flat file for insertion into a spreadsheet application or database management system for further analysis. ProbeLynx has been initially developed to focus on arrays derived from human, mouse, chicken and bovine genomes, but may be expanded to handle other genomic datasets. ProbeLynx offers microarray users the important ability to continuously assess the potential of a probe to cross-hybridize to paralogous genes and the suitability of a given probe to investigate a transcript of interest. By also including the latest gene function annotation information in the output, ProbeLynx provides the critical first step in updating microarray data annotation.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.065 | 0.044 |
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".