<scp>miRNA</scp> regulation in the context of functional protein networks: principles and applications
Bibliographic record
Abstract
MicroRNAs (miRNAs) are small non‐coding endogenous regulatory RNAs that fine‐tune gene expression in a wide range of biological processes and diseases. miRNAs exert their function by targeting mRNAs to trigger their degradation or inhibit protein translation. The proteins encoded by the genes targeted by miRNAs may act as key components of cellular networks, thus the use of biological molecular network information for the purposes of elucidating the role of miRNAs in molecular disease mechanism is a key objective in systems biomedicine. The crosstalk layer between miRNA–target networks and functional protein is rich sources of information to explore the function of miRNAs at the system level. Characterizing the influence of miRNAs in the context of the target (protein interactors of the target) is in the early stages with potential to help better understand how miRNAs function within the cellular networks. In this article, the latest research on the cross‐talk between miRNAs and protein networks, particularly physical protein interactions and gene regulatory networks is summarized. This article also covers recent research on understanding the biology of miRNAs at the system level and defines principles of miRNA regulation of protein and gene regulatory networks. The second part of the article highlights the promise of considering the protein context of the miRNA target when searching for functional miRNA–target interactions. Some of the applications of integrating protein networks with miRNA–targets that have clinical and functional utility are described. WIREs Syst Biol Med 2014, 6:189–199. doi: 10.1002/wsbm.1251 This article is categorized under: Analytical and Computational Methods > Computational Methods Laboratory Methods and Technologies > Macromolecular Interactions, Methods Biological Mechanisms > Regulatory Biology Laboratory Methods and Technologies > RNA Methods
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".