REPRESENTATION LEARNING OF PROTEOME DYNAMICS TO CHARACTERIZE ORGANISMIC COMMUNICATION AND INTRINSIC HEALTH
Bibliographic record
Abstract
Abstract Co-regulation and interactions among organ systems are crucial to maintain health. Therefore, understanding patterns of such organismic communication using proteome dynamics could be an innovative and effective strategy for quantifying intrinsic human health. In this preliminary study, we conducted a case-control study involving six participants with severe genetic mitochondrial disease and six age- and sex-matched healthy subjects. We measured and recorded the salivary proteome of each participant (n=2922 proteins) at 0-, 30-, and 45-minutes post-awakening as well as bedtime (pm), over two days, representing a natural physiological challenge known to involve systems-wide physiological recalibrations. We then analyzed the changes in protein levels across successive time points using a high-dimensional two-sample testing approach (Cai, Liu, and Xia 2013). The protein co-regulation pattern (captured by the covariance matrix of their changes in expression) was significantly different between the healthy and mitochondrial disease participants (p=0.01), thereby linking proteome dynamics with health status. We then implemented Weighted Correlation Network Analysis to identify representative features showing a significant correlation with the subject’s health status between pairs of successive time points (0-30, 30-45, 45-pm). The correlation between the extracted feature and the self-rated health was as high as 0.39 (p-value=0.03) for the 30-45 time period. This finding reveals an association between health status and salivary protein co-regulation patterns, and suggests a promising representation learning strategy to quantify the manifestation of health into accessible proteome dynamics.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".