Protein kinases: Role of their dysregulation in carcinogenesis, identification and inhibition
Bibliographic record
Abstract
Protein kinases belong to the phosphor-transferases superfamily of enzymes, which "activate" enzymes via phosphorylation. The kinome of an organism is the total set of genes in the genome, which encode for all the protein kinases. Certain mutations in the kinome have been linked to dysregulation of protein kinases, which in turn can lead to several diseases and disorders including cancer. In this review, we have briefly discussed the role of protein kinases in various biochemical processes by categorizing cancer associated phenotypes and giving their protein kinase examples. Various techniques have also been discussed, which are being used to analyze the structure of protein kinases, and associate their roles in the oncogenesis. We have also discussed protein kinase inhibitors and United States Federal Drug Administration (USFDA) approved drugs, which target protein kinases and can serve as a counter to protein kinase dysregulation and mitigate the effects of oncogenesis. Overall, this review briefs about the importance of protein kinases, their roles in oncogenesis on dysregulation and how their inhibition via various drugs can be used to mitigate their effects.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".