Guest Editorial—Special Issue on '-Omics' Based Companion Diagnostics for Personalized Medicine
Bibliographic record
Abstract
Companion diagnostics are essential to the success of personalized medicine. With the rapid advances made in high throughput molecular biology, such as genomics, proteomics, and metabolomics, in the past two decades, scientists, researchers and engineers are beginning to harvest the power of '-omics' to develop companion diagnostic circuits and systems. These systems can be used to diagnose, monitor or predict not just a single disease, but multiple diseases simultaneously. This allows for the management of disease at a personal level, i.e., accordingly to the biology, needs and lifestyle of individual patients. Foreseeing this emerging trend, we are pleased to present this special issue to: (i) provide a road map of '-omics' networks, circuits and systems; (ii) encourage cross-disciplinary collaboration in this emerging research field; and (iii) report the cutting edge development of these circuits and devices with potential for translation into the clinic. A number of submissions were received, covering a wide range of circuits and systems related '-omics' topics, such as (i) DNA, RNA, protein, and small molecule sensors for companion diagnostics; (ii) micro/nanofluidics technologies related to '-omics'; and (iii) circuit based modeling and simulation of '-omics' systems including gene regulatory and signaling networks. After rigorous peer review nine papers were finally selected in this special issue. The papers can be broadly divided into three groups and briefly introduced below.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.055 | 0.028 |
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".