RNA sequencing-based histological subtyping of non-small cell lung cancer with generative adversarial data imputation
Bibliographic record
Abstract
Non small cell lung cancer (NSCLC) is the most common type of lung cancer and is classified into two main histological subtypes: adenocarcinoma and squamous cell carcinoma. The identification of the histological subtype is a crucial step in the diagnosis of NSCLC. RNA sequencing data hold valuable biological information but may contain missing gene expression counts, which limit their potential exploitation in practice. In this work, we address the issue of missing gene expression data in NSCLC histological subtype prediction from RNA sequencing. To this end, we propose a pipeline based on the generative adversarial imputation network (GAIN) for the generation of plausible imputations of missing data and tree-based ensemble models for NSCLC histological subtype prediction. We adopted a nested cross validation scheme for the evaluation of the classification models. The proposed pipeline exhibited an outstanding performance with an area under the receiver operating characteristic curve of 0.98 ± 0.03 and an accuracy of 0.96 ± 0.05 obtained with the Light Gradient Boosting Machine. Experimental results showed that GAIN-derived imputations are useful to boost classification performance. Finally, we used the Shapley Additive Explanations technique and found a set of genes that were the most relevant for NSCLC subtyping across different models.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".