Regulation of gene expression: probing DNA–protein interactions<i>in vivo</i>and<i>in vitro</i>
Bibliographic record
Abstract
Tremendous efforts have been put together over the last several years to complete the entire sequencing of the human genome. As we enter the proteomic era, when the major aim is understanding which gene encodes which protein, the time has also come to identify their precise function inside the astonishing signaling network required to accomplish all cellular functions. Understanding when, why and how a gene is expressed has now become a necessity toward identifying all the regulatory pathways that mediate cellular processes such as differentiation, migration, replication, DNA repair and apoptosis. Regulation of gene transcription is a process that is primarily under the influence of nuclear-located transcription factors. Consequently, identifying which protein activates or represses a specific gene is a prerequisite for understanding cell fate and function. The current state of, and recent advances in, transcriptional regulation approaches are reviewed here, with special emphasis on new technologies required when probing for DNA-protein interactions. This review explores different strategies aimed at identifying both the regulatory sequences of any given gene and the trans-acting regulatory factors that recognize these elements as their target sites in the nucleus. Ongoing developments in the fields of nanotechnology, RNA silencing and protein modeling toward the investigation of DNA-protein interactions and their relevance in the battle against cancer are discussed.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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".