Editorial: Application of genomics and epigenetics in disease and syndrome classification
Bibliographic record
Abstract
Editorial on the Research Topic Application of genomics and epigenetics in disease and syndrome classificationGenomics and epigenetics have revolutionized disease diagnosis and classification, providing comprehensive insights into disease categorizations (Shen et al., 2018;Koelsche et al., 2021;de Leval et al., 2022).By integrating genomics and epigenetics from the concept of central dogma vs. paracentral dogma (Wang, 2023a; Wang, 2023b), we could account for both genetic predisposition and environmental influences on disease development.In precision medicine, syndrome differentiation plays a crucial role in disease diagnosis and treatment (Dai et al., 2022).Exploring therapeutic strategies to specific syndromes enables more effective disease management.The incorporation of symptomatology and customized prescriptions contributes significantly to the advancement of precision medicine.We are proud to showcase four featured publications in this Research Topic entitled "Application of Genomics and Epigenetics in Disease and Syndrome Classification".This editorial aims to provide a concise overview of these articles and highlight their significant contributions to the field.Genomic studies play a pivotal role in identifying genetic variations associated with diseases.Notably, genome-wide association studies have unveiled single nucleotide polymorphisms (SNPs) associated with specific diseases (Wang et al., 2023).In this Research Topic, Liu et al. found that miR-196a2 rs11614913 and miR-27a rs895819 may influence genetic susceptibility to gastric precancerous lesions (GPL) or gastric cancer (GC).Additionally, they revealed a synergistic effect between miR-196a2 rs11614913 and Helicobacter pylori infection in the onset and progression of GPL.A similar study previously indicated that pri-miR-124-1 rs531564 and STAT3 rs1053023 are associated with a higher risk of GC (Mirnoori et al., 2018).These studies demonstrated that SNPs of miRNAs could be significantly associated with
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.014 | 0.021 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".