Illumination of understudied ciliary kinases
Bibliographic record
Abstract
Cilia are cellular signaling hubs. Given that human kinases are central regulators of signaling, it is not surprising that kinases are key players in cilia biology. In fact, many kinases modulate ciliogenesis, which is the generation of cilia, and distinct ciliary pathways. Several of these kinases are understudied with few publications dedicated to the interrogation of their function. Recent efforts to develop chemical probes for members of the cyclin-dependent kinase like (CDKL), never in mitosis gene A (NIMA) related kinase (NEK), and tau tubulin kinase (TTBK) families either have delivered or are working toward delivery of high-quality chemical tools to characterize the roles that specific kinases play in ciliary processes. A better understanding of ciliary kinases may shed light on whether modulation of these targets will slow or halt disease onset or progression. For example, both understudied human kinases and some that are more well-studied play important ciliary roles in neurons and have been implicated in neurodevelopmental, neurodegenerative, and other neurological diseases. Similarly, subsets of human ciliary kinases are associated with cancer and oncological pathways. Finally, a group of genetic disorders characterized by defects in cilia called ciliopathies have associated gene mutations that impact kinase activity and function. This review highlights both progress related to the understanding of ciliary kinases as well as in chemical inhibitor development for a subset of these kinases. We emphasize known roles of ciliary kinases in diseases of the brain and malignancies and focus on a subset of poorly characterized kinases that regulate ciliary biology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".