Harvesting the Genome's Bounty: Integrative Genomics
Bibliographic record
Abstract
In this post-genomic era, we face the daunting challengeof assigning function to tens of thousands of uncharacterized open reading frames. Furthermore, the physical andfunctional connections between gene products must beelucidated if we are to understand how the linear DNA sequence encodes dynamic cellular function. An unexpectedresult of the Human Genome Project is the low number ofpredicted human genes, roughly fivefold more than in single-celled yeasts and approximately double that in flies orworms (Lander et al. 2001; Venter et al. 2001). Increasedconnectivity between this relatively constant number ofgenes may underlie the massive increase in system-levelcomplexity that distinguishes yeast from humans.Genome-wide approaches to discovery of gene functionnow include systematic analysis of genetic interactions,protein interactions, genome-wide expression profiles, andmutant phenotypes. More than any single approach, eachof which is subject to caveats in interpretation and reproducibility, the intersection of orthogonal genome-scaledata sets provides a robust means to interrogate gene function. Here, we summarize advanced genome-scale methods for biological discovery in the budding yeast Saccharomyces cerevisiae, many of which will be transportable tointerrogation of human gene function...
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.010 |
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".