Bibliographic record
Abstract
Title: Understanding and Drugging the Epigenome and the Non-Coding Transcriptome Abstract This lecture will primarily be concerned with long noncoding natural antisense RNAs which regulate gene expression through several distinct mechanisms including modulation of chromatin regulator protein complexes. Notably, inhibition/perturbation of endogenous natural antisense transcripts by AntagoNATs, in vitro (1) or in vivo (2), often reveals discordant regulation and results in locus specific up-regulation of conventional (protein-coding) gene expression. References 1. Katayama S, … and Wahlestedt C. Antisense transcription in the mammalian transcriptome. Science 309:1564–1566, 2005. 2. Modarresi F, Faghihi MA, Lopez-Toledano MA, Fatemi RP, Magistri M, Brothers SP, van der Brug MP and Wahlestedt C. Natural antisense inhibition results in transcriptional de-repression and gene up-regulation. Nature Biotechnology, 25;30:453–9, 2012. 3. Wahlestedt C. Targeting noncoding RNA to therapeutically up-regulate gene expression. Nature Reviews Drug Discovery 12:433–46, 2013. 4. Yamanaka Y, Faghihi MA, Magistri M, Alvarez-Garcia O, Lotz M, Wahlestedt C. Antisense RNA controls sense transcript expression through interaction with a chromatin-associated protein, HMGB2. Cell Rep, 11:967–976, 2015.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.294 | 0.152 |
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".