{"id":"W2150515766","doi":"10.1093/bioinformatics/btq305","title":"Efficient learning of microbial genotype–phenotype association rules","year":2010,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Biology; Phenotype; Association (psychology); Computational biology; Phylogenetic tree; Genotype; Genome-wide association study; Association rule learning; Genetic association; Genotype-phenotype distinction; Gene; Genetics; Data mining; Computer science; Single-nucleotide polymorphism; Psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001952708,0.00006779362,0.00007203092,0.00003722922,0.00005320728,0.00001380814,0.00009878782,0.0001399522,0.000032802],"category_scores_gemma":[0.0001469904,0.00006195088,0.00004607735,0.00005637989,0.00002190859,0.000001780438,0.0000427608,0.0001047751,0.00004160003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000119202,"about_ca_system_score_gemma":0.00005960533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000274316,"about_ca_topic_score_gemma":0.000002513455,"domain_scores_codex":[0.9994442,0.00001482259,0.0002337295,0.00007405496,0.0001201071,0.0001130996],"domain_scores_gemma":[0.9994231,0.000007173723,0.0002477542,0.0001647475,0.0001221698,0.00003505801],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000172794,0.00002381245,0.001559839,0.00002022178,0.00001215654,2.449402e-8,0.000155089,0.0003809143,0.9909236,0.0002895259,0.002020433,0.004597135],"study_design_scores_gemma":[0.0006773915,0.0001446654,0.01208009,0.00001479783,0.00002658422,0.000002671655,0.0002171073,0.0206777,0.7690082,0.00003002517,0.1968701,0.0002506282],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.986525,0.00004008566,0.008313331,0.00004449766,0.0003757912,0.00008460881,0.000009759784,0.00001250061,0.004594409],"genre_scores_gemma":[0.9900779,0.00002050561,0.008911309,0.0000565466,0.0001247221,0.000003565487,0.00009363598,0.000007911088,0.0007038883],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2219154,"threshold_uncertainty_score":0.2526284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006223568153281153,"score_gpt":0.226096786178602,"score_spread":0.2198732180253209,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}