DENetwork: Unveiling Regulatory and Signaling Networks Behind Differentially-Expressed Genes
Bibliographic record
Abstract
ABSTRACT Differential gene expression analysis from RNA-sequencing (RNA-seq) data offers crucial insights into biological differences between sample groups. However, the conventional focus on differentially-expressed (DE) genes often omits non-DE regulators, which are an integral part of such differences. Moreover, DE genes frequently serve as passive indicators of transcriptomic variations rather than active influencers, limiting their utility as intervention targets. To address these shortcomings, we have developed DENetwork . This innovative approach deciphers the intricate regulatory and signaling networks driving transcriptomic variations between conditions with distinct phenotypes. Unique in its integration of both DE and critical non-DE genes in a graphical model, DENetwork enhances the capabilities of traditional differential gene analysis tools, such as DESeq2 . Our application of DENetwork to an array of simulated and real datasets showcases its potential to encapsulate biological differences, as demonstrated by the relevance and statistical significance of enriched gene functional terms. DENetwork offers a robust platform for systematically characterizing the biological mechanisms that underpin phenotypic differences, thereby augmenting our understanding of biological variations and facilitating the formulation of effective intervention strategies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".