Systematic Proteome and Transcriptome Analysis of Stem Cell Populations
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
Methods for relative assessment of the transcriptional activity of the cell are now routinely employed and obtain large amounts of information regarding process such as transformation or development. These approaches have great impact and are of significant value. Nonetheless, mRNA is an intermediate in the process of protein synthesis and changes in mRNA expression do not reflect absolute or relative changes in protein levels. The mechanisms which translate mRNA to protein are highly regulated, and it remains unclear how the transcriptome reflects the functional state of the cell, as defined by its protein output. Large scale analyses of the proteome are now becoming a reality due to technical advances in protein arrays and mass spectrometry. Thus for the first time data on large numbers of mRNA transcripts and the levels of expression of their associated proteins is available in dynamic systems. Analysis of one such comparison, the transcriptome and proteome of primary haematopoietic stem cells, reveals post-translational regulation of the proteome in stem cell populations. The factors which must be considered when comparing two systematically acquired 'omics' datasets are reviewed and the relative merits of transcriptome and proteome approaches are discussed.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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