Prediction of Bioactive Metabolites from American <i>Aconitum</i> Using Network Integrating Cellular Morphological Profiling and Mass Spectrometry Data
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Asian and American Aconitum species are phylogenetically close, but only certain Asian species have been well-studied for their medicinal properties. This study aims to discover bioactive compounds in two American Aconitum species based on a systematic networking strategy integrating both mass spectrometry data and biological profiles from a high-throughput phenotypic screening assay, Cell Painting. The chemical profiles of four different plant parts of two American Aconitum species ( A. columbianum and A. uncinatum ) were obtained by ion mobility mass spectrometry and compared with two Asian ( A. carmichaelii and A. fischeri ), and one European species ( A. napellus ). Biological screening, image analysis, and feature extraction were performed on Aconitum extracts using the Cell Painting assay. The results provided 2,090 unique morphological features per extract, which were further reduced to 429. In conjunction with 4,400 chemicals from a library with known mechanisms of action, 198 unique hierarchical clusters were established. An overall activity heuristic called CP score was calculated for each sample. After integrating the CP score and spectrometric data, a network filtered for higher CP scores was constructed and the compounds with high activity were tentatively identified. The network contained mostly American Aconitum species, suggesting that these understudied plants produce useful bioactive compounds.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".