Intelligent decision support for protein crystal growth
Bibliographic record
Abstract
Current structural genomics projects are likely to produce hundreds of proteins a year for structural analysis. The primary goal of our research is to speed up the process of crystal growth for proteins in order to enable the determination of protein structure using single crystal X-ray diffraction. We describe Max, a working prototype that includes a high-throughput crystallization and evaluation setup in the wet laboratory and an intelligent software system in the computer laboratory. A robotic setup for crystal growth is able to prepare and evaluate over 40 thousand crystallization experiments a day. Images of the crystallization outcomes captured with a digital camera are processed by an image-analysis component that uses the two-dimensional Fourier transform to perform automated classification of the experiment outcome. An information repository component, which stores the data obtained from crystallization experiments, was designed with an emphasis on correctness, completeness, and reproducibility. A case-based reasoning component provides support for the design of crystal growth experiments by retrieving previous similar cases, and then adapting these in order to create a solution for the problem at hand. While work on Max is still in progress, we report here on the implementation status of its components, discuss how our work relates to other research, and describe our plans for the future.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".