Computational prediction of eukaryotic phosphorylation sites
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
MOTIVATION: Kinase-mediated phosphorylation is the central mechanism of post-translational modification to regulate cellular responses and phenotypes. Signaling defects associated with protein phosphorylation are linked to many diseases, particularly cancer. Characterizing protein kinases and their substrates enhances our ability to understand and treat such diseases and broadens our knowledge of signaling networks in general. While most or all protein kinases have been identified in well-studied eukaryotes, the sites that they phosphorylate have been only partially elucidated. Experimental methods for identifying phosphorylation sites are resource intensive, so the ability to computationally predict potential sites has considerable value. RESULTS: Many computational techniques for phosphorylation site prediction have been proposed, most of which are available on the web. These techniques differ in several ways, including the machine learning technique used; the amount of sequence information used; whether or not structural information is used in addition to sequence information; whether predictions are made for specific kinases or for kinases in general; and sources of training and testing data. This review summarizes, categorizes and compares the available methods for phosphorylation site prediction, and provides an overview of the challenges that are faced when designing predictors and how they have been addressed. It should therefore be useful both for those wishing to choose a phosphorylation site predictor for their particular biological application, and for those attempting to improve upon established techniques in the future. CONTACT: brett.trost@usask.ca.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it