Identification and characterization of novel human tissue-specific RFX transcription factors
Bibliographic record
Abstract
BACKGROUND: Five regulatory factor X (RFX) transcription factors (TFs)-RFX1-5-have been previously characterized in the human genome, which have been demonstrated to be critical for development and are associated with an expanding list of serious human disease conditions including major histocompatibility (MHC) class II deficiency and ciliaophathies. RESULTS: In this study, we have identified two additional RFX genes-RFX6 and RFX7-in the current human genome sequences. Both RFX6 and RFX7 are demonstrated to be winged-helix TFs and have well conserved RFX DNA binding domains (DBDs), which are also found in winged-helix TFs RFX1-5. Phylogenetic analysis suggests that the RFX family in the human genome has undergone at least three gene duplications in evolution and the seven human RFX genes can be clearly categorized into three subgroups: (1) RFX1-3, (2) RFX4 and RFX6, and (3) RFX5 and RFX7. Our functional genomics analysis suggests that RFX6 and RFX7 have distinct expression profiles. RFX6 is expressed almost exclusively in the pancreatic islets, while RFX7 has high ubiquitous expression in nearly all tissues examined, particularly in various brain tissues. CONCLUSION: The identification and further characterization of these two novel RFX genes hold promise for gaining critical insight into development and many disease conditions in mammals, potentially leading to identification of disease genes and biomarkers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".