A Comprehensive Oncological Biomarker Framework Guiding Precision Medicine
Bibliographic record
Abstract
Cancer remains a major cause of mortality worldwide, driving ongoing innovation in therapeutic strategies. Immunotherapy has transformed cancer care by leveraging the immune system to target tumors, but its effectiveness is limited by tumor heterogeneity, immune resistance, and unpredictable toxicities. Moreover, the absence of robust biomarkers to predict therapeutic response and manage adverse effects remains a significant challenge. Recent advances in biomarker discovery, including liquid biopsy technologies and gut microbiota profiling, are enhancing the precision of immunotherapy and enabling more personalized cancer management. Here, we present a Comprehensive Oncological Biomarker Framework that integrates genetic and molecular testing, imaging, histopathology, multi-omics, and liquid biopsy to generate a molecular fingerprint for each patient. This holistic approach supports individualized diagnosis, prognosis, treatment selection, and response monitoring. Incorporating emerging biomarkers, such as microbiome signatures, further refines patient stratification, guiding the optimization of therapy. By uniting molecular insights with clinical and social factors, this framework aims to address tumor heterogeneity and immune evasion, ultimately improving patient outcomes through precision oncology.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".