The humankind genome: from genetic diversity to the origin of human diseases
Bibliographic record
Abstract
Genome-wide association studies have failed to establish common variant risk for the majority of common human diseases. The underlying reasons for this failure are explained by recent studies of resequencing and comparison of over 1200 human genomes and 10 000 exomes, together with the delineation of DNA methylation patterns (epigenome) and full characterization of coding and noncoding RNAs (transcriptome) being transcribed. These studies have provided the most comprehensive catalogues of functional elements and genetic variants that are now available for global integrative analysis and experimental validation in prospective cohort studies. With these datasets, researchers will have unparalleled opportunities for the alignment, mining, and testing of hypotheses for the roles of specific genetic variants, including copy number variations, single nucleotide polymorphisms, and indels as the cause of specific phenotypes and diseases. Through the use of next-generation sequencing technologies for genotyping and standardized ontological annotation to systematically analyze the effects of genomic variation on humans and model organism phenotypes, we will be able to find candidate genes and new clues for disease's etiology and treatment. This article describes essential concepts in genetics and genomic technologies as well as the emerging computational framework to comprehensively search websites and platforms available for the analysis and interpretation of genomic data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".