Bibliographic record
Abstract
The development of next-generation sequences has brought not only high-throughput sequencing but also new possibilities for various kinds of analysis methods of genetic information. Dr. Hayashizaki et al. developed new technologies to construct the full-length cDNA library and applied them to high-throughput sequencing technologies for large-scale transcriptome analysis. These analysis results overturned the conventional assumption the 2% of the genome is transcribed by showing that 70% or more of the genome is transcribed as RNA through FANTOM activities which was founded in 2000 on their initiative. Further, the existence of 23,000 non-protein coding RNAs was confirmed. These new findings redefine the central dogma into a new picture containing new interaction cascade and the unexpected complexity of combined omics. The neo central dogma shows that there are three types of final products derived from genes; long ncRNA, small ncRNA, and protein. They play essential roles by forming complexes with each other to maintain life. Long ncRNA and small ncRNA play a role as a ligand with sequence information. Long ncRNA and protein play a role as a functional molecule. Here, I would like to introduce the neo central dogma concept and some of the mechanisms of ncRNAs.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".