The Concept of Multiple-Target Anti-miRNA Antisense Oligonucleotide Technology
Bibliographic record
Abstract
The multiple-target AMO technology or MT-AMO technology is an innovative strategy, which confers on a single AMO fragment the capability of targeting multiple miRNAs. This modified AMO is single-stranded 2'-O-methyl-modified oligoribonucleotides carrying multiple AMO units, which are engineered into a single unit and are able to simultaneously silence multiple-target miRNAs or multiple miRNA seed families. Studies suggest that the MT-AMO is an improved approach for miRNA target finding and miRNA function validation; it not only enhances the effectiveness of targeting miRNAs but also confers diversity of actions. It has been successfully used to identify target genes and cellular function of several oncogenic miRNAs and of the muscle-specific miRNAs (Lu et al., Nucleic Acids Res 37:e24-e33, 2009). This novel strategy may find its broad application as a useful tool in miRNA research for exploring biological processes involving multiple miRNAs and multiple genes, and the potential as an miRNA therapy for human disease such as cancer and cardiac disorders. This technology was developed by my research laboratory in collaboration with Yang's group (Lu et al., Nucleic Acids Res 37:e24-e33, 2009), and it is similar but distinct from the miRNA Sponge technology developed by Sharp's laboratory in 2007 (Ebert et al., Nat Methods 4:721-726, 2007) and modified by Gentner et al. (Nat Methods 6:63-66, 2009).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".