Are the tools fit for purpose? Network inference algorithms evaluated on a simulated lipidomics network
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
Motivation: Various methods have been proposed to construct metabolic networks from metabolomic data; however, small sample sizes, multiple confounding factors, the presence of indirect interactions as well as randomness in metabolic processes are of major concern. Results: In this study, we benchmark existing algorithms for creating correlation- and regression-based networks of changes in metabolite abundance and evaluate their performance across different sample sizes of a generative model. Using standard interaction-level tests and network-scale analyses based on centrality scores, we assess how well these methods recover represented metabolomic networks. Our findings reveal significant challenges in network inference and result interpretation, even when sample sizes are significant and data are the result of computer modeling of metabolic pathways. Despite these limitations, we demonstrate that correlation-based network inference can, to some extent, discriminate between two different metabolic states in the computational model. This suggests potential utility in distinguishing overarching changes in metabolic processes but not direct pathways in different conditions. Availability and implementation: All relevant data is provided at https://github.com/TheCOBRALab/metabolicRelationships.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it