Bibliographic record
Abstract
Summary The success of the genomics revolution to construct a genetic architecture of a variety of model organisms has placed functional biologists under pressure to show what each individual gene does in vivo . Traditionally, this task has fallen on geneticists who systematically perturb gene function and study the consequences. With the advent of large, easily accessible, small‐molecule libraries and new methods of chemical synthesis, biologists now have new ways to probe gene function. Often called chemical genetics, this approach involves the screening of compounds that perturb a process of interest. In this scenario, each perturbing chemical is analogous to a specific mutation. Here, we summarize, with specific examples, how chemical genetics is being used in combination with traditional genetics to address problems in plant biology. Because chemical genetics is rooted in genetic analysis, we focus on how chemicals used in combination with genetics can be very powerful in dissecting a process of interest. Contents Summary 15 I. Introduction 15 II. Lessons from the past: genetics 17 III. No time like the present (or ‘I want a new drug’): forward chemical genetics 18 IV. Back to the future: chemical genomics 21 V. Target identification: biochemistry 22 VI. Conclusions and perspective 24 References 25
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".