Conducting genetic interaction analysis with CRISPR-Cas9-based strategies in Candida albicans
Bibliographic record
Abstract
Candida albicans is an opportunistic fungal pathogen found in the oral mucosa, the gut, the vaginal mucosa, and humans' skin. While C. albicans can cause superficial infections, severe invasive infections can occur in immunocompromised individuals. Understanding the survival mechanisms and pathogenesis of C. albicans is critical for novel antifungal drug discovery. Determining the relationships between different genes can create a genetic interaction map, which can identify complementary gene sets, central to C. albicans survival, as potential drug targets in combination therapy. A genetic approach using the CRISPR-Cas9-based genome editing platform will focus on genetic interaction analysis of C. albicans stress response genes. The ultimate goal is to create a stress response gene deletion library to study its pathogen survival role. This library of single and double stress response gene mutants will be screened under diverse growth conditions to assess their relative fitness. Genetic interaction analysis will help map out epistatic interactions between fungal genes involved in growth, survival, and pathogenesis and uncover putative targets for combination antifungal therapy based on negative or synthetic lethal genetic interactions.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 0.001 |
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".