Systematic review and meta-analysis of interstitial lung disease transcriptomics
Bibliographic record
Abstract
Background: Interstitial lung diseases (ILDs), which encompass >200 diseases involving excess deposition of extracellular matrix (ECM) in the lung, have been extensively characterized by whole genome expression sequencing (transcriptomics). However, a consensus of molecular aberrations in ILD has yet to be determined. We aimed to identify a molecular signature of ILD subtypes through integration of data obtained from transcriptomic-profiled lung samples. Methods: A literature search was conducted in two databases (MEDLINE and EMBASE) to identify 5,337 publications, which were then screened to keep 18 studies involving ILD transcriptomics. Microarray and RNA-seq data from these studies were obtained from the Gene Expression Omnibus and integrated via the Multivariate INTegration (MINT) framework. Results: We identified 17 studies examining lung samples from subjects with the ILD subtype idiopathic pulmonary fibrosis (IPF). Gene expression data was integrated via MINT and used to develop an IPF classification model (Figure 1; 14 datasets) consisting of 30 genes (e.g. MMP7, COL3A1, CXCL14), which was validated on 4 test datasets with an AUC of 0.96. Similar models were developed for hypersensitivity pneumonitis (HP; 3 datasets; 99 genes) and systemic sclerosis-associated ILD (SSc-ILD; 2 datasets; 11 genes). Conclusion: We derived molecular signatures of 3 ILD subtypes, which may be used to improve diagnostic and therapeutic approaches.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.026 |
| Bibliometrics | 0.013 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".