From data to discovery: leveraging big data in plant natural products biosynthesis research
Bibliographic record
Abstract
Plant natural products or specialized metabolites play a vital role in drug discovery and development, with many clinically important derivatives such as the anticancer drugs topotecan (derived from the natural alkaloid camptothecin) and etoposide (derived from the natural polyphenol podophyllotoxin). Remarkable advances in understanding plant natural product metabolism have been achieved at an unprecedented pace over the past 15 years. The integration of high-throughput technologies in genomics, transcriptomics, and metabolomics has generated vast datasets that provide a more comprehensive understanding of plant metabolism. Additionally, advances in computational tools, machine learning, and data analytics have played a crucial role in processing and interpreting the massive amounts of newly available data, enabling researchers to uncover intricate regulatory networks and identify key components of biosynthetic pathways. This review navigates the evolving landscape of plant biosynthetic pathway elucidation accelerated by innovative multidisciplinary strategies that capitalize on big data. We highlight recent advances in plant-specialized biosynthesis that illustrate how big data are increasingly leveraged to unravel the complexities of plant metabolism.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".