Past, Present, and Future of Molecular and Cellular Oncology
Bibliographic record
Abstract
In the last 20 years, the field of cellular and molecular oncology has been born and has moved its first steps, with an increasingly rapid pace. Hundreds of oncogenic and oncosuppressive signaling cascades have been characterized, facilitating the development of an ever more refined and variegated arsenal of diagnostic and therapeutic weapons. Furthermore, several cancer-specific features and processes have been identified that constitute promising therapeutic targets. For instance, it has been demonstrated that microRNAs can play a critical role in oncogenesis and tumor suppression. Moreover, it turned out that tumor cells frequently exhibit an extensive metabolic rewiring, can behave in a stem cell-like fashion (and hence sustain tumor growth), often constitutively activate stress response pathways that allow them to survive, can react to therapy by engaging in non-apoptotic cell death programs, and sometimes die while eliciting a tumor-specific immune response. In this Perspective article, we discuss the main issues generated by these discoveries that will be in the limelight of molecular and cellular oncology research for the next, hopefully few years.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".