The Endocrine Society of Australia Proceedings 2006/The New Zealand Society of Endocrinology Proceedings 2004 and 2005-2
Bibliographic record
Abstract
It appears that we have fundamentally misunderstood the nature of genetic programming in humans and other multicellular organisms for the past 50 years because of the presumption, largely correct in prokaryotes but not in complex eukaryotes, that most genetic information is transacted by proteins, which form the main analog components of all cells. Humans have the same number of protein coding genes (19,500) as the nematode worm (~19,300), which has only 1,000 cells. Although only 1.2% of the human genome encodes proteins, the vast majority is actually transcribed in a developmentally regulated fashion, much of it on both strands. These transcripts include tens if not hundreds of thousands of small RNAs, including miRNAs, snoRNAs, piRNAs and other yet-to-be-discovered classes of regulatory RNAs, many of which are encoded in introns, and longer noncoding RNAs that exhibit dynamic expression patterns during germ cell and ES cell differentiation, gonadal development, muscle development, brain development, and macrophage and T-cell activation, to name a few. Many are dysregulated in disease, including neurological diseases and cancer. It is also now evident that most, if not all, complex genetic phenomena in the higher organisms are directed by RNA signaling pathways. Taken together, the data suggest that most of the human genome and those of other complex organisms, including transposon-derived sequences, is not junk nor evolving neutrally, but rather encodes a hitherto hidden layer of regulatory RNAs (many of which are species-or lineage-specific) that set the settings and direct the trajectories of differentiation and development via the control of chromatin architecture and epigenetic memory, promoter selection, splicing, RNA modification and editing, and mRNA stability and translation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".