sRNARFTarget: a machine learning-based approach for sRNA target prediction
Bibliographic record
Abstract
Bacterial small regulatory RNAs (sRNAs) play a vital role in the regulation of gene expression in bacteria. sRNAs regulate gene expression by interacting with mRNAs or proteins. Bacterial sRNAs are involved in various processes, such as environmental stress response, metabolism, and virulence. We need to identify the mRNAs and/or proteins that these sRNAs interact with, to understand the functional roles of sRNAs. These mRNAs or proteins are called targets of the sRNAs. There are several computational tools available for sRNA target prediction; however, these tools have a high number of false positives, and the most accurate tool requires sRNA sequence conservation across bacteria. As a result of this research project, a machine-learning-based method (sRNARFTarget) for sRNA target prediction applicable to any bacterium or sRNA has been developed. In this thesis, we show that sRNARFTarget substantially outperforms current non-comparative genomics-based methods in terms of running time and ranking of true interactions.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".