Generative artificial intelligence GPT-4 accelerates knowledge mining and machine learning for synthetic biology
Bibliographic record
Abstract
Abstract Knowledge mining from synthetic biology journal articles for machine learning (ML) applications is a labor-intensive process. The development of natural language processing (NLP) tools, such as GPT-4, can accelerate the extraction of published information related to microbial performance under complex strain engineering and bioreactor conditions. As a proof of concept, we used GPT-4 to extract knowledge from 176 publications on two oleaginous yeasts ( Yarrowia lipolytica and Rhodosporidium toruloides ). After integration with a molecule inventory database, the outcome is a total of 2037 data instances and 28 features, which serve as machine learning inputs. The structured datasets enabled ML approaches (e.g., a random forest model) to predict Yarrowia fermentation titers with high accuracy (R 2 of 0.86 for unseen test data). Via transfer learning, the trained model could also assess the production capability of the non-conventional yeast, R. toruloides , for which there are fewer published reports. This work demonstrated the potential of generative artificial intelligence to speed up information extraction from research articles, thereby improving design-build-test-learn (DBTL) cycles for commercial biomanufacturing development.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".