Bibliographic record
Abstract
Cancer develops from normal cells upon the accumulation of mutations. Two majors types of cancer genes are altered in human tumor cells, oncogenes and tumor suppressors. Activation of oncogenes cooperates with inactivation of tumor suppressors to fully transform cells. One of the major tumor suppressor pathways in humans is the so-called RB pathway. RB acts as a cellular brake, when its gene is deleted or when there are alterations in proteins that control RB function, then cells divide abnormally. After nearly three decades of study, the role of the RB pathway in cancer has been well established and inactivation of RB has been found in a large number of human cancers,including lung cancer as well as many cancers associated with cigarette smoking. Research on RB remains intense with publication of nearly 1,000 relevant journal articles a year, but major challenges in the RB field include determining the different roles of the multiple RB-containing complexes in cells, exploring the importance of novel biological functions for RB and their relationship to tumor suppression, as well as identifying novel therapeutic approaches to stop or slow the growth of human tumor cells with mutations in the RB pathway. In particular, because RB is not an enzyme, there are no simple ways to inhibit its activity in cells; in addition, RB controls the expression of a large number of other genes and it is unclear which function of RB is important for the inhibition of cancer. One major hurdle in the RB field has been the lack of scientific interactions between members of this community. While some RB researchers sometimes meet at scientific conferences focusing on cancer or cell cycle, these conferences often have a limited number of presentations on RB and its pathway. With this in mind, Drs. Zacksenhaus and Bremner organized the first two International RB meeting in 2009 and 2011 in Toronto. The meetings were deemed a success and help solidy a large group of investigators with a strong interest in participating in a scientific meeting focusing on RB and its regulatory networks, which would be organized every other year in a rotating manner by active participants. The 2013 meeting will be co-organized by Dr. Sage (PI, Stanford University, CA) and Dr. Seth Rubin (co-PI, UC Santa Cruz, CA). The major goal of this meetign will be to enhance the opportunities to exchange ideas between researchers in the field, incluidng by presenting recent and unpublished data. Having the meeting in Monterey will help recruit new investigators to the field and the conference and will raise the visibility of research being conducted in California on lung cancer and other cancers associated with cigarette smoking.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.384 | 0.343 |
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".