Jumping on the Train of Personalized Medicine: A Primer for Non- Geneticist Clinicians: Part 1. Fundamental Concepts in Molecular Genetics
Bibliographic record
Abstract
With the decrease in sequencing cost and the rise of companies providing sequencing services, it is likely that personalized whole-genome sequencing will eventually become an instrument of common medical practice. We write this series of three reviews to help non-geneticist clinicians get ready for the major breakthroughs that are likely to occur in the coming years in the fast-moving field of personalized medicine. This first paper focuses on the fundamental concepts of molecular genetics. We review how recombination occurs during meiosis, how de novo genetic variations including single nucleotide polymorphisms (SNPs), insertions and deletions are generated and how they are inherited from one generation to the next. We detail how genetic variants can impact protein expression and function, and summarize the main characteristics of the human genome. We also explain how the achievements of the Human Genome Project, the HapMap Project, and more recently, the 1000 Genomes Project, have boosted the identification of genetic variants contributing to common diseases in human populations. The second and third papers will focus on genetic epidemiology and clinical applications in personalized medicine. Keywords: Chromosome, deoxyribonucleic acid, genetic variants, haplotype, human genome, linkage disequilibrium.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.012 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 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".