An upstream interacting context based framework for the computational inference of microRNA functions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the rapid accumulation of microRNA (miRNAs), a class of newly identified small noncoding RNAs, in silico inference of miRNA functions has become one of the central tasks in miRNA bioinformatics. Traditional methods have helped in the understanding of miRNAs, but they also have limitations. In this paper, we first gave a brief review for the progress of bioinformatic methods in miRNA function inference and next presented a new framework (miRUPnet) for inferring the functions of miRNAs by functional analysis of a novel dimension of miRNA network, the context of its transcription factors (TFs) in a protein-protein interaction network. This dimension represents specific biological processes initiated by TF combinations and therefore differs from traditional methods in concept. To validate the accuracy of our method, we first comprehensively mined literature-reported miRNA functions and then made a comparison with the prediction result. The results show that even using the stringent TFBS rule, our method has independently predicted 68.2% of the literature reported miRNA functions, suggesting that miRUPnet has a high accuracy. Moreover, our approach successfully predicted specific functions that could not be inferred for given miRNAs using traditional methods. More importantly, it can distinguish miRNAs from the same family, as well as those present in multiple copies that cannot be differentiated through traditional methods. This study presents a new concept and dimension for miRNA function inference. miRUPnet represents an important and novel method for inferring the function of miRNAs. miRUPnet is available at http://cmbi.bjmu.edu.cn/mirupnet.
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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 | 0.000 | 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 it