Methods and approaches to disease mechanisms using systems kinomics
Bibliographic record
Abstract
All cellular functions, ranging from regular cell maintenance and homeostasis, specialized functions specific to cellular types, or generating responses due to external stimulus, are mediated by proteins within the cell. Regulation of these proteins allows the cell to alter its behavior under different circumstances. A major mechanism of protein regulation is utilizing protein kinases and phosphatases; enzymes that catalyze the transfer of phosphates between substrates [1]. Proteins involved in phosphate signaling are well studied and include kinases and phosphatases that catalyze opposing reactions regulating both structure and function of the cell. Kinomics is the study of kinases, phosphatases and their targets, and has been used to study the functional changes in numerous diseases and infectious diseases with aims to delineate the cellular functions affected. Identifying the phosphate signaling pathways changed by certain diseases or infections can lead to novel therapeutic targets. However, a daunting 518 putative protein kinase genes have been identified [2], indicating that this protein family is very large and complex. Identifying which enzymes are specific to a particular disease can be a laborious task. In this review, we will provide information on large-scale systems biology methodologies that allow global screening of the kinome to more efficiently identify which kinase pathways are pertinent for further study.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".