Proteomics of human embryonic stem cells
Bibliographic record
Abstract
Human embryonic stem cells (hESCs) offer exciting potential in regenerative medicine for the treatment of a host of diseases including cancer, Alzheimer's and Parkinson's disease. They also provide insight into human development and disease and can be used as models for drug discovery and toxicity analyses. The key properties of hESCs that make them so promising for medical use are that they have the ability to self-renew indefinitely in culture and they are pluripotent, which means that they can differentiate into any of more than 200 human cell types. Since proteins are the effectors of cellular processes, it is important to investigate hESC expression at the protein level as well as at the transcript level. In addition, post-translational modifications, such as phosphorylation, may influence the activity of pivotal proteins in hESCs, and this information can only be determined by studying the proteome. In this review, we summarize the results obtained from several proteomics analyses of hESCs that have been reported in the last few years.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".