Multiomics and Systems Biology Are Needed to Unravel the Complex Origins of Chronic Disease
Bibliographic record
Abstract
Modernization has now been linked to poor developmental experience, the onset of immune dysregulation and rising rates of chronic diseases in many parts of the world. Research across the epidemiological, clinical, and basic science domains supports the concept that poor developmental experience, particularly during prenatal life, can increase the risk of chronic disease, with enduring effects on long-term health. Single ‘omics’ approaches are ill-suited to dealing with the level of complexity that underpins immune dysregulation in early life. A more comprehensive systems-level view is afforded by combining multiple ‘omics’ datasets in order to delineate correlations across multiple resolutions of the genome, and of the genomes of the microorganisms that inhabit us. In this concept paper, we discuss multiomic approaches to studying immune dysregulation and highlight some of the challenges and opportunities afforded by this new domain of medical science.
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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 | 0.014 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".