Biological Networks Governing the Acquisition, Maintenance, and Dissolution of Pluripotency: Insights from Functional Genomics Approaches
Bibliographic record
Abstract
The repertoire of transcripts encoded by the genome contributes to the diversity of cellular states. Functional genomics aims to comprehensively uncover the roles of these transcripts to reconstruct biological networks and transform this information into useful knowledge. High-throughput functional screening has served as a powerful genetic discovery tool by enabling massively parallel implementation of biological assays. In recent years, high-throughput screening has unearthed crucial players in the regulation of different aspects of pluripotency, which is a unique property that enables a cell to differentiate into multiple cell types of the three major lineages. Pluripotency thus represents an interesting biological paradigm for studying the acquisition, maintenance, and dissolution of cellular states. In this review, we highlight the major findings of high-throughput studies to dissect these three aspects of pluripotency for the mouse and human systems. Collectively, they provide new insights into cell fate maintenance and transition. In addition, we also discuss the opportunities and challenges awaiting high-throughput screening in the future.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".