The transcriptome of human CD34 <sup>+</sup> hematopoietic stem-progenitor cells
Bibliographic record
Abstract
Studying gene expression at different hematopoietic stages provides insights for understanding the genetic basis of hematopoiesis. We analyzed gene expression in human CD34(+) hematopoietic cells that represent the stem-progenitor population (CD34(+) cells). We collected >459,000 transcript signatures from CD34(+) cells, including the de novo-generated 3' ESTs and the existing sequences of full-length cDNAs, ESTs, and serial analysis of gene expression (SAGE) tags, and performed an extensive annotation on this large set of CD34(+) transcript sequences. We determined the genes expressed in CD34(+) cells, verified the known genes and identified the new genes of different functional categories involved in hematopoiesis, dissected the alternative gene expression including alternative transcription initiation, splicing, and adenylation, identified the antisense and noncoding transcripts, determined the CD34(+) cell-specific gene expression signature, and developed the CD34(+) cell-transcription map in the human genome. Our study provides a current view on gene expression in human CD34(+) cells and reveals that early hematopoiesis is an orchestrated process with the involvement of over half of the human genes distributed in various functions. The data generated from our study provide a comprehensive and uniform resource for studying hematopoiesis and stem cell biology.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 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".