The emerging importance of DNA mapping and other comprehensive screening techniques, as tools to identify new drug targets and as a means of (cancer) therapy personalisation
Bibliographic record
Abstract
Every human being is genetically unique and this individuality is not only marked by morphologic and physical characteristics but also by an individual's response to a particular drug. Single nucleotide polymorphisms (SNPs) are largely responsible for one's individuality. A drug may be ineffective in one patient, whereas the exact same drug may cure another patient. Recent advances in DNA mapping and other screening technologies have provided researchers and drug developers with crucial information needed to create drugs that are specific for a given individual. In the future, physicians will be able to prescribe individualised drugs adjusted to, for example, activities of specific enzymatic pathways that would either be targeted by these drugs, or would be responsible for drug conversion or inactivation. Furthermore, the mapping of the human genome allows broader development and application of drugs that act on the level of gene transcription rather than as simple biochemical inhibitors or activators of certain enzymes. Such new approaches will maximise desired therapeutic results and may completely eliminate severe side effects. To illustrate the potential of genetic translational research, the authors discuss available analytical methodologies such as; gene arrays, flow cytometry-based screening for SNPs, proteomics, metabolomics, real-time PCR, and other methods capable of detecting both SNPs, as well as more profound changes in cell metabolism. Finally, the authors provide several examples that focus mostly on targeting protein-DNA interactions, but also other processes.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".