Multiplexed and microparticle‐based analyses: Quantitative tools for the large‐scale analysis of biological systems
Bibliographic record
Abstract
While the term flow cytometry refers to the measurement of cells, the approach of making sensitive multiparameter optical measurements in a flowing sample stream is a very general analytical approach. The past few years have seen an explosion in the application of flow cytometry technology for molecular analysis and measurements using microparticles as solid supports. While microsphere-based molecular analyses using flow cytometry date back three decades, the need for highly parallel quantitative molecular measurements that has arisen from various genomic and proteomic advances has driven the development in particle encoding technology to enable highly multiplexed assays. Multiplexed particle-based immunoassays are now common place, and new assays to study genes, protein function, and molecular assembly. Numerous efforts are underway to extend the multiplexing capabilities of microparticle-based assays through new approaches to particle encoding and analyte reporting. The impact of these developments will be seen in the basic research and clinical laboratories, as well as in drug development.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.006 |
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".