Signed Distance Correlation (SiDCo): A network analysis application of distance correlation for identifying metabolic networks disrupted in Dementia with Lewy Bodies
Bibliographic record
Abstract
Abstract Motivation Identifying pathological metabolic changes in complex disease such as Dementia with Lewy Bodies (DLB) requires a deep understanding of functional modifications in the context of metabolic networks. Network determination and analysis from metabolomics and lipidomics data remains a major challenge due to sparse experimental coverage, a variety of different functional relationships between metabolites and lipids, and only sporadically described reaction networks. Results Distance correlation, measuring linear and non-linear dependences between variables as well as correlation between vectors of different lengths, e.g. different sample sizes, is presented as an approach for data-driven metabolic network development. Additionally, novel approaches for the analysis of changes in pair-wise correlation as well as overall correlations for metabolites in different conditions are introduced and demonstrated on DLB data. Distance correlation and signed distance correlation was utilized to determine metabolic network in brain in DLB patients and matching controls and results for the two groups are compared in order to identify metabolites with the largest functional change in their network in the disease state. Novel correlation network analysis showed alterations in the metabolic network in DLB brains relative to the controls, with the largest differences observed in O -phosphocholine, fructose, propylene-glycol, pantothenate, thereby providing novel insights into DLB pathology only made apparent through network investigation with presented methods.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".