Advances and applications of clinical proteomics in precision medicine
Bibliographic record
Abstract
INTRODUCTION: Clinical proteomics has become a pivotal component of precision medicine, significantly advancing the understanding of disease mechanisms and informing therapeutic strategies. This review explores how clinical proteomics is transforming diagnostic and therapeutic approaches across multiple fields. AREAS COVERED: This review highlights recent developments and applications of clinical proteomics in cardiovascular and neurological disorders, as well as its impact on drug development. Technologies such as mass spectrometry and protein microarrays have enhanced diagnostic precision, facilitated the discovery of novel biomarkers, and uncovered new therapeutic targets. In cardiovascular medicine, proteomics supports early disease detection and patient risk stratification, while in neurology, it helps identify disease-specific protein signatures that guide targeted interventions. The integration of proteomics with databases like Universal Protein Resource (UniProt) and the Human Protein Atlas, alongside the use of advanced bioinformatics tools, has streamlined data analysis and accelerated the design of personalized therapies. EXPERT OPINION: Clinical proteomics is rapidly evolving, offering unprecedented opportunities to refine diagnostics, personalize therapies, and improve patient outcomes. Overcoming current challenges in standardization and validation will be essential for its full integration into clinical practice.
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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 | 0.025 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".