Multi-omics data integration for topology-based pathway activation assessment and personalized drug ranking
Bibliographic record
Abstract
Although multi-omics analysis is popular for revealing diverse physiological effects and biomarkers in many branches of state-of-the-art molecular and cell biology and bioinformatics, there is still no consensus on a gold standard protocol for the integration of various multi-omics profiles into a uniformly shaped system bioinformatics platform. In the current study, we performed the integration of data on DNA methylation, and the expression of coding RNA (mRNA), micro-RNA (miRNA), and long non-coding RNA into a joint platform for calculation of signaling pathway impact analysis (SPIA) and drug efficiency index (DEI). We found that the mirrored and balanced DEI values fitted the DNA methylome data better than the original DEI. Additionally, the protein-coding mRNA-based values correlated more strongly with antisense lncRNA-based values than with miRNA-based values. The whole correlation between the mRNA-based and antisense lncRNA-based values was generally positive. This platform allowed integrative analysis of several levels of gene expression regulation of protein-coding genes and their regulators, including methylation and noncoding RNAs.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".