Validation of Recombinant Antibodies Against Human Transcription Factors
Bibliographic record
Abstract
The Recombinant Antibody Network (RAN) is a pilot project involving 3 integrated automation centres to create renewable, open‐source, high‐quality binding reagents. The main aims of RAN are to: generate rAbs (recombinant antibodies) directed to the ~1500 human transcription factors (TFs) against ~3000 domain targets using high‐throughput selection methods; validate and produce 蠅2 rAbs per target for high performance in applications such as immunofluorescence (IF), western blot, and chromatin‐immunoprecipitation; distribute validation data to our network and the scientific community through a dedicated web portal; and continually improve the efficiency, speed, cost and success. The RAN pipeline progressively identifies clones that bind target domains with high affinity and specificity. Our rAbs should be able to bind and IP full‐length cellular TFs from complex mixtures, and demonstrate specificity between family members. Additional validation steps include over‐expression of the target, knock‐down of the target by RNAi, and altered growth conditions. A key step in the validation process is high‐content screening against a panel of 6 Human cell lines to monitor cellular localization. The rAbs that show nuclear localization by IF are tested by immunoprecipitation‐mass spectrometry for their ability to pull‐down endogenous target. To date, over 1200 rAbs against >250 targets have been monitored by IF, with approximately 25% showing nuclear localization patterns.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".