Seeking a better understanding of the non-receptor tyrosine kinase, SRMS
Bibliographic record
Abstract
SRMS (Src-Related kinase lacking C-terminal regulatory tyrosine and N-terminal Myristoylation Sites) is a non-receptor tyrosine kinase first reported in a 1994 screen for genes regulating murine neural precursor cells. SRMS, pronounced "Shrims", lacks the C-terminal regulatory tyrosine critical for the regulation of the enzymatic activity of Src-family kinases (SFKs). Another remarkable characteristic of SRMS is its localization into distinct SRMS cytoplasmic punctae (SCPs) or GREL (Goel Raghuveera-Erique Lukong) bodies, a pattern not observed in the SFKs. This unique subcellular localization of SRMS could dictate its cellular targets, proteome, and potentially, substrates. However, the function of SRMS is still relatively unknown. Further, how is its activity regulated and by what cellular targets? Studies have emerged highlighting the potential role of SRMS in autophagy and in regulating the activation of BRK/PTK6. Potential novel cellular substrates have also been identified, including DOK1, vimentin, Sam68, FBKP51, and OTUB1. Recent studies have also demonstrated the potential role of the kinase in various cancers, including gastric and colorectal cancers and platinum resistance in ovarian cancer. This review discusses the advancements made in SRMS-related biology to date and the path to understanding the cellular and physiological significance of the kinase.
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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 | 0.003 |
| 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".