Approaches and developments in studying the human microbiome network
Bibliographic record
Abstract
The human microbiome is dynamic and unique to each individual, and its role is being increasingly recognized in healthy physiology and in disease, including gastrointestinal and neuropsychiatric disorders. Therefore, characterizing the human microbiome and the factors that shape its bacterial population, how they are related to host-specific attributes, and understanding the ways in which it can be manipulated and the phenotypic consequences of such manipulations are of great importance. Characterization of the microbiome so far has been mostly based on compositional studies alone, where relative abundances of different species are compared in different conditions, such as health and disease. However, inter-relationships among the bacterial species, such as competition and cooperation over metabolic resources, may be an important factor that affects the structure and function of the microbiome. Here we review the network-based approaches in answering such questions and explore the first attempts that focus on the interactions facet, complementing compositional studies, towards understanding the microbiome structure and its complex relationship with the human host.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".