Efficient learning of microbial genotype–phenotype association rules
Bibliographic record
Abstract
MOTIVATION: Finding biologically causative genotype-phenotype associations from whole-genome data is difficult due to the large gene feature space to mine, the potential for interactions among genes and phylogenetic correlations between genomes. Associations within phylogenetically distinct organisms with unusual molecular mechanisms underlying their phenotype may be particularly difficult to assess. RESULTS: We have developed a new genotype-phenotype association approach that uses Classification based on Predictive Association Rules (CPAR), and compare it with NETCAR, a recently published association algorithm. Our implementation of CPAR gave on average slightly higher classification accuracy, with approximately 100 time faster running times. Given the influence of phylogenetic correlations in the extraction of genotype-phenotype association rules, we furthermore propose a novel measure for downweighting the dependence among samples by modeling shared ancestry using conditional mutual information, and demonstrate its complementary nature to traditional mining approaches. AVAILABILITY: Software implemented for this study is available under the Creative Commons Attribution 3.0 license from the author at http://kiwi.cs.dal.ca/Software/PICA
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".