Identifying microRNA precursors using linear dimensionality reduction with explicit feature mapping
Bibliographic record
Abstract
MicroRNAs are a class of small RNAs of about 20 nucleotides long, which regulate cellular processes in animals and plants. Identifying microRNAs is one of the important tasks in microRNA and transcriptional studies. The main signal that is used for identifying these tiny molecules is the hairpin secondary structure of microRNA precursors. In this research, I propose to use a linear dimensionality reduction(LDR)-based classifier to identify precursor microRNAs from both pseudo hairpins and other non-coding RNAs. LDR has been shown to be widely used in machine learning and pattern recognition problems. Due to the complexity of the data and nature of the problem, linear-based classifiers might not have an acceptable performance. Therefore, I propose to use explicit mapping to project data onto a higher dimensional space in order to increase class separability. Feature selection methods are used in order to reduce the complexity of the classifier and find relevant biological descriptors.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".