Meeting report – 9th IRIC International Symposium on Molecular Targets in Cancer Genomics
Bibliographic record
Abstract
Graduate students and postdoctoral fellows at the Institute for Research in Immunology and Cancer (IRIC) organized the 9th IRIC International Symposium on 14-15 May, 2015. The symposium was held at the IRIC, an ultra-modern research hub and training center located on the hilltop of the Université de Montréal campus in Montreal, Canada. This year's title was 'Molecular Targets in Cancer Genomics', reflecting the common interest of the IRIC student community. Through four broadly themed sessions, organizers sought to highlight the new generation of anti-cancer strategies including targeted therapies directed against actionable cancer-specific mutations, and immunotherapies, which enhance immune responses against cancer. Both targeted and immunotherapies are tailored to cancer-specific features, and require precise knowledge of cancer cells, from their genome to their proteome. The focus of this symposium was on translating the molecular basis of cancer into a functional understanding of aberrant pathways, and to uncover novel targets to be exploited for cancer therapeutic strategies.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.041 | 0.023 |
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".