RNA Three-Dimensional Structures, Computer Modeling of
Bibliographic record
Abstract
The knowledge of the 3-D structure of an RNA molecule gives us indications about which other molecules it can interact with, and how its biochemical function is achieved or can be modified. Unfortunately, direct structure determination methods, such as X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, and electron microscopy, cannot be applied at the rate of the genomics era yet, limiting the deciphering of RNA folding and structure. The recent resolution of the large ribosomal subunit (LSU) crystal structure represented a leaping step, but it has mainly pointed out the weaknesses of the current methods for analyzing and inspecting RNA 3-D structures. At this time, RNA sequences of predetermined function cannot be designed and folded properly. Further analysis and interpretations of the available 3-D structures are thus needed to bring additional insights into RNA function. Here, we present state-of-the-art data structures and algorithms that were developed in the last 10 years to analyze and inspect efficiently and objectively RNA 3-D structures. As you will realize by reading the following sections, they are the same as those employed in the development of the best computer modeling methods of RNA 3-D structures. Keywords: Base Pairing; Base Stacking; Graph; Leadzyme; MC-Sym ; Motif; Spanning Tree
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 | 0.000 | 0.001 |
| 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) | 0.007 | 0.004 |
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".