Bibliographic record
Abstract
The breadth and scope of Fibroblast Growth Factor signaling is immense, with documentation of its role in almost every organism and system studied so far. FGF ligands signal through a family of four distinct tyrosine kinase receptors, the FGF receptors (FGFRs). One contribution to the diversity of function and signaling of FGFs and their receptors arises from the numerous alternative splicing variants that have been documented in the FGFR literature. The present review discusses the types and roles of alternatively spliced variants of the FGFR family members and the significant impact of alternative splicing on the physiological functions of five broad classes of FGFR isoforms. Some characterized known regulatory mechanisms of alternative splicing and future directions in studies of FGFR alternative splicing are also discussed. Presence, absence, and/or the combination of specific exons within each FGFR protein impart upon each individual isoform its unique function and expression pattern during normal function and in diseased states (e.g., in cancers and birth defects). A better understanding of the diversity of FGF signaling in different developmental contexts and diseased states can be achieved through increased knowledge of the presence of specific FGFR isoforms and their impact on downstream signaling and functions. Modern high-throughput techniques afford an opportunity to explore the distribution and function of isoforms of FGFR during development and in diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".