Bibliographic record
Abstract
MicroRNA (miRNA) are short, non-coding RNAs involved in cell regulation at posttranscriptional and translational levels.MiRNA act on messenger RNA through a silencing mechanism, affecting biological activities such as cell cycle control, biological development, differentiation, and stress response.Experimental validation of predicted miRNA is a time and cost expensive procedure, as it requires wet-lab experiments.Therefore, a variety of computational approaches have been developed to increase prediction accuracy and reduce validation costs.While these methods are highly effective, they require large labelled training data sets, which are often not available for many species.Simultaneously, emerging wet-lab experimental procedures are becoming available that produce large unlabelled data sets of genomic sequence and RNA expression profiles.Existing methods are unable to leverage these unlabelled data.This thesis explores two emerging trends in semi-supervised machine learning to maximize the utility of both labelled and unlabelled training data.Specifically, this thesis explores the application of active learning and multi-view co-training to microRNA prediction for the first time.Results show that our active learning approach is able to greatly improve classification performance using a small number of labeled instances, outperforming state-of-the-art methods under equivalent training data constraints.Multi-view co-training results also demonstrate improved performance compared to single view classifiers and yield high classification performance using a minimum number of labeled instances for classification.This thesis demonstrates that semi-supervised machine learning is likely be useful in creating predictors of novel miRNA, particularly for species where few training exemplars are available.mRNA Messenger RNA NGS Next Generation Sequencing RNA Ribonucleic
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".