From data to discovery: leveraging big data in plant natural products biosynthesis research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.000 | 0.002 |
| 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 it