Gene-Gene Interaction Tests Using SVM and Neural Network Modeling
Bibliographic record
Abstract
Artificial neural networks (ANN) and support vector machine (SVM) modeling offer promise in the analysis of genotype-phenotype correlation in genetic association studies. In particular, we are interested in studying single nucleotide polymorphisms (SNPs) as genetic markers as predictors of a dichotomous disease outcome. The problem we are investigating is that of gene-gene and gene-environment interactions as determinants of the expression of complex diseases. This study builds on our previous work for a single gene testing procedure developed and presented earlier (Matchenko-Shimko and Dube, 2006). As for single SNPs pre-selection (Matchenko-Shimko and Dube, 2006), we rely on ANN sensitivity analysis algorithms to detect potential pairs of interacting SNPs associated with the disease outcome. The statistical test for SNP interaction is computed using a bootstrap technique and is based on the measure of the predictive significance of two SNPs from the change in the ANN error function (SVM regression error) when these two SNPs are removed from the ANN or SVM genotype-phenotype models. To investigate the power to detect and test gene-gene interactions we simulated genotypes including two interacting loci with low marginal effects, incomplete penetrance and phenocopies according to three different models of interaction
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".