A novel motif-discovery algorithm to identify co-regulatory motifs in large transcription factor and microRNA co-regulatory networks in human
Bibliographic record
Abstract
MOTIVATION: Interplays between transcription factors (TFs) and microRNAs (miRNAs) in gene regulation are implicated in various physiological processes. It is thus important to identify biologically meaningful network motifs involving both types of regulators to understand the key co-regulatory mechanisms underlying the cellular identity and function. However, existing motif finders do not scale well for large networks and are not designed specifically for co-regulatory networks. RESULTS: In this study, we propose a novel algorithm CoMoFinder to accurately and efficiently identify composite network motifs in genome-scale co-regulatory networks. We define composite network motifs as network patterns involving at least one TF, one miRNA and one target gene that are statistically significant than expected. Using two published disease-related co-regulatory networks, we show that CoMoFinder outperforms existing methods in both accuracy and robustness. We then applied CoMoFinder to human TF-miRNA co-regulatory network derived from The Encyclopedia of DNA Elements project and identified 44 recurring composite network motifs of size 4. The functional analysis revealed that genes involved in the 44 motifs are enriched for significantly higher number of biological processes or pathways comparing with non-motifs. We further analyzed the identified composite bi-fan motif and showed that gene pairs involved in this motif structure tend to physically interact and are functionally more similar to each other than expected. AVAILABILITY AND IMPLEMENTATION: CoMoFinder is implemented in Java and available for download at http://www.cs.utoronto.ca/∼yueli/como.html.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".