Bioinformatics advances for clinical biomarker development
Bibliographic record
Abstract
INTRODUCTION: Bioinformatics tools, techniques and resources are critical to biomarker discovery, assessment, validation, qualification, standardization and market acceptance into clinical practice. Huge scientific effort and economic investment over the past 20 years have resulted in thousands of new candidate biomarkers for diseases, yet relatively few biomarkers have entered clinical practice. Bioinformatics is central to all stages of biomarker development and implementation. AREAS COVERED: This review examines bioinformatics advances that bear on each stage of biomarker development and suggests bioinformatics strategies to assist biomarkers towards clinical practice. This paper focuses on the steps of clinical biomarker development with an emphasis on the review literature from 2000 to June 2011. The intent of this paper is to describe the present role of bioinformatics in biomarker development including the controversies associated with various developmental stages. EXPERT OPINION: The key message is that more effective biomarker development requires database input of higher quality, improved bioinformatics tools to identify more clearly the acceptable criteria for each development step, as well as more and better database linkages.
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 | 0.031 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".