Bibliographic record
Abstract
Approximately one-third of all deaths are attributed to cardiovascular diseases, and ischaemic heart disease is the primary cause of death globally.Hence there continues to be a pressing need for advances in preventative, diagnostic, and interventional strategies for cardiovascular disease.The multifactorial nature of cardiovascular disease, which encompasses both genetic and environmental factors, presents a major challenge to research in these areas.Furthermore, personalised strategies, which are anticipated to hold the key to reducing cardiovascular mortality rates, require yet more sophisticated biomarkers capable of accurately predicting disease risk, informing clinical decision-making, or monitoring therapeutic responsiveness.Omic approaches, including genomics, transcriptomics, proteomics, and metabolomics, exploit genome information and use high-throughput analysis techniques.Because the data are comprehensive and the measurements are performed in parallel (i.e., reducing interassay variability), it lends itself to interrogation by bioinformatic tools that provide objective "unbiased"/"unsupervised" assessment of pertinent features, functional clusters, interactions, and regulatory networks.The marriage between comprehensive data collection and computational analysis is proven as powerful and pragmatic means of discovering new information and candidate biomarkers, which might not otherwise have been investigated.This editorial aims to orientate the reader with regard to where proteomic studies sit amongst the other levels of investigation (i.e., genomics, transcriptomics, etc.) and draw attention to the particular strengths and challenges involved in proteomic work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".