Welcome to the machine: how machine learning identified metabolomic changes in <i>Brachypodium distachyon</i> under stress
Bibliographic record
Abstract
Plants produce an incredible array of specialized metabolites that are used to attract pollinators, deter herbivores, communicate with each other, and regulate their own growth and physiology.It has been estimated that plants as a whole produce over one million different metabolites (Afendi et al., 2011), but investigations into these chemicals remain difficult due to an inability to identify unique compounds from metabolomic data.As the primary means of annotating mass spectrometry data are to compare peaks with previously described standards, less than 5% of plant metabolites can be identified in untargeted metabolomic experiments (da Silva et al., 2015).While working as a postdoctoral researcher with Robert Last at Michigan State University, Gaurav Moghe used molecular biochemistry, mass spectrometry, and computational techniques to study the biosynthesis of acylsugars, which are specialized metabolites found in the Solanaceae.This involved hand-annotating each acylsugar peak obtained from the mass spectrometer, which took a substantial amount of time and labor.Once he started his own lab at Cornell University, Moghe wondered how he could improve the identification of these peaks using computational approaches.He and his PhD student Elizabeth Mahood found that rule-based annotations and molecular networking approaches could be useful for specific experiments.For example, they wrote Python scripts to identify anthocyanin/flavonoid-like peaks from sweet potatoes (Ipomoea batatas) (Bennett et al., 2021).However, this rulebased approach could not be used for the broader plant metabolome, which is much more complex, diverse, and unknown.To overcome this hurdle, Mahood started using a new machine learning tool called CANOPUS to predict structural classes of metabolites (D€ uhrkop et al., 2021).Mahood's research was part of a larger Joint Genome Institute (JGI)-sponsored project attempting to correlate transcriptomic and metabolomic data to better annotate metabolic gene function in the model grass Brachypodium distachyon.The goal was to devise a set of experiments
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".