Editorial: Multi-omics: Trends and applications in clinical research
Bibliographic record
Abstract
Editorial on the Research Topic Multi-omics: Trends and applications in clinical researchOmics-based studies have substantially contributed to the emergence and progress of systems biology over the past decades.Each layer of omics data provides a unique perspective on the biological system at a particular molecular level (e.g., RNA, proteins, metabolites), with all levels being involved in different yet inter-related biological processes.However, data at any individual omics level cannot fully explain how these different multi-layered biological processes interact and how they lead to emergence of complex phenotypes.To answer these questions, multi-omics strategies have been increasingly employed to obtain a broader view of the interactions that connect genotypes to phenotypes of interest.Omics data integration can provide a more reliable and holistic picture of the biochemistry and dynamics of biological systems, as compared to data from any omics layer alone.Multi-omics is also a powerful tool for discovery and prioritization of biomarkers, which is a topic of high interest in clinical research.To understand biological processes within the systems biology context, various statistical and computational tools are typically employed in multi-omics studies.In clinical studies, data integration across different omics is a promising tool for the early detection of various diseases, as well as for evaluating the efficacy of different treatments.Therefore, the aim of this Research Topic was to cover advances in the multi-omics research field with consideration of clinical applications.This Research Topic of five articles involves multi-omics studies of different diseases.By integrating genomics and transcriptomics data, Wang et al. evaluated the effect of smoking history on cancer patients and proposed a 46-gene model to predict overall survival of the patients, as well as to disease-specific survival and progression-free intervals.To understand esophageal squamous cell carcinoma (ESCC), Zang et al. combined comprehensive metabolomic characterization with expression profiling of interleukin enhancer binding factor 2 (ILF2) and ILF3, proteins known to contribute to the occurrence and development of several types of malignancy.The results indicated
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.016 | 0.013 |
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".