Informative Bayesian Model Selection: a method for identifying interactions in genome-wide data
Bibliographic record
Abstract
In high-dimensional genome-wide (GWA) data, a key challenge is to detect genomic variants that interact in a nonlinear fashion in their association with disease. Identifying such genomic interactions is important for elucidating the inheritance of complex phenotypes and diseases. In this paper, we introduce a new computational method called Informative Bayesian Model Selection (IBMS) that leverages correlation among variants in GWA data due to the linkage disequilibrium to identify interactions accurately in a computationally efficient manner. IBMS combines several statistical methods including canonical correlation analysis, logistic regression analysis, and a Bayesians statistical measure of evaluating interactions. Compared to BOOST and BEAM that are two widely used methods for detecting genomic interactions, IBMS had significantly higher power when evaluated on synthetic data. Furthermore, when applied to Alzheimer's disease GWA data, IBMS identified previously reported interactions. IBMS is a useful method for identifying variants in GWA data, and software that implements IBMS is freely available online from http://lbb.ut.ac.ir/Download/LBBsoft/IBMS.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".