The Importance of Human Tissue Bioresources in Advancing Biomedical Research
Bibliographic record
Abstract
Medical research advances enabling the realization of precision medicine have relied heavily on the biospecimens provided by bioresources to identify the targets and biomarkers that are the focus of the new generation of more effective molecular-based therapies for specific subtypes of diseases. Through the biospecimens they have distributed, bioresources have permitted subtypes of cancers to be identified and molecular features of these subtypes to be effectively targeted. A prototype example is the human epidermal growth factor receptor type 2 (HER2), which currently is targeted in breast and gastric cancers. In the future, the use of biospecimens from bioresources will continue to increase the understanding of the molecular actions of drugs and how drugs may be more or less active in subpopulations of patients. Although the biospecimen inventories of the initial forms of bioresources may not have always been optimally planned and, therefore, utilized in supporting biomedical research, bioresources are evolving and overall, bioresource inventories and increasingly their prospective collection capabilities will continue to be a critical component of the research infrastructure.
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 | 0.020 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".