Genomic analysis of induced pluripotent stem (iPS) cells: routes to reprogramming
Bibliographic record
Abstract
The phenomenal proliferation of scientific studies into the nature of induced pluripotent stem (iPS) cells following publication of the findings of Takahashi and Yamanaka little more than 2 years ago, have significantly expanded our understanding of cellular mechanisms relating to cell lineage, differentiation, and proliferation. While the full potential of iPS cell lineages for both scientific tool and therapeutic applications is as yet unclear, findings from several lines of investigation suggests that multipotential and terminally differentiated cells from an array of cell types are competent to undergo epigenetic reprogramming to a pluripotential state. The nature of this pluripotential state appears to be similar to, but not identical with that previously described for embryonic stem (ES) cells. Understanding the nature of this induced reprogrammed state will be critical to determining the full potential of iPS cells. Recently, this issue has been examined through an integrated analysis of the genome in fully and partially reprogrammed iPS cell lineages. These results provide a window onto the temporal components of reprogramming and suggest mechanisms by which the efficacy of reprogramming can be enhanced.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".