Application of CART to the *interaction
Bibliographic record
Abstract
Copyright information:Taken from "Methodological issues in detecting gene-gene interactions in breast cancer susceptibility: a population-based study in Ontario"http://www.biomedcentral.com/1741-7015/5/22BMC Medicine 2007;5():22-22.Published online 7 Aug 2007PMCID:PMC1976420. CART sequentially partitions the data into two homogeneous subsets: first using -[Lys751Gln], {AA} versus {AC, CC}; and then the {AA} subset is split according to -[G(-1082)A], {AA, AG} versus {GG}. The splitting variables leading to such groups are inherent main or interaction effects. For example, a low-risk subgroup is defined by the two SNPs: -[Lys751Gln] and -[G(-1082)A] and the tree suggests an interaction between these two SNPs. Multi-way interactions can be detected in a similar way. The terminal nodes can be classified as low- or high-risk subgroups (indicated by different color density) and their association with the outcome can be estimated (that is, the corresponding odds ratio is 0.63 with a -value of 0.002). Therefore, investigating the tree terminal nodes provides a natural way to identify interactions and characterize high- or low-risk subgroups.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.164 | 0.025 |
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".