Identification of Novel Lung Genes in Bronchial Epithelium by Serial Analysis of Gene Expression
Bibliographic record
Abstract
A description of the transcriptome of human bronchial epithelium should provide a basis for studying lung diseases, including cancer. We have deduced global gene expression profiles of bronchial epithelium and lung parenchyma, based on a vast dataset of nearly two million sequence tags from 21 serial analysis of gene expression (SAGE) libraries from individuals with a history of smoking. Our analysis suggests that the transcriptome of the bronchial epithelium is distinct from that of lung parenchyma and other tissue types. Moreover, our analysis has identified novel bronchial-enriched genes such as MS4A8B, and has demonstrated the use of SAGE for the discovery of novel transcript variants. Significantly, gene expression associated with ciliogenesis is evident in bronchial epithelium, and includes the expression of transcripts specifying axonemal proteins DNAI2, SPAG6, ASP, and FOXJ1 transcription factor. Moreover, expression of potential regulators of ciliogenesis such as MDAC1, NYD-SP29, ARMC3, and ARMC4 were also identified. This study represents a comprehensive delineation of the bronchial and parenchyma transcriptomes, identifying more than 20,000 known and hypothetical genes expressed in the human lung, and constitutes one of the largest human SAGE studies reported to date.
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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".