Individual-level analysis of differential expression of genes and pathways for personalized medicine
Bibliographic record
Abstract
MOTIVATION: The differential expression analysis focusing on inter-group comparison can capture only differentially expressed genes (DE genes) at the population level, which may mask the heterogeneity of differential expression in individuals. Thus, to provide patient-specific information for personalized medicine, it is necessary to conduct differential expression analysis at the individual level. RESULTS: We proposed a method to detect DE genes in individual disease samples by using the disrupted ordering in individual disease samples. In both simulated data and real paired cancer-normal sample data, this method showed excellent performance. It was found to be insensitive to experimental batch effects and data normalization. The landscape of stable gene pairs in a particular type of normal tissue could be predetermined using previously accumulated data, based on which dysregulated genes and pathways for any disease sample can be readily detected. The usefulness of the RankComp method in clinical settings was exemplified by the identification and application of prognostic markers for lung cancer. AVAILABILITY AND IMPLEMENTATION: RankComp is implemented in R script that is freely available from Supplementary Materials.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".