SGAM: An Array-Based Approach for High-Resolution Genetic Mapping in Saccharomyces cerevisiae
Bibliographic record
Abstract
The development of genome-scale resources and high-throughput methodologies has enabled systematic assessment of gene function in vivo. Synthetic genetic array (SGA) analysis automates yeast genetic manipulation, permitting diverse analysis of approximately 5,000 viable deletion mutants in Saccharomyces cerevisiae. SGA methodology has enabled genome-wide synthetic lethal screening and construction of a large-scale genetic interaction network for yeast. Genetic networks often reveal new components of specific pathways and functional relationships between genes whose products buffer one another or impinge on a common essential pathway. Because SGA analysis can be used to manipulate any genetic element linked to a selectable marker, it is a highly versatile approach that can be adapted for a variety of different genetic screens, including synthetic lethality, dosage suppression, and dosage lethality. This chapter focuses on a specific SGA application for high-resolution genetic mapping, referred to as SGA mapping (SGAM), which enables the identification of suppressor mutations and thus provides a powerful means for interrogating gene function and pathway order.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".