{"id":"W2110067331","doi":"10.1109/cibcb.2007.4221209","title":"Gene-Gene Interaction Tests Using SVM and Neural Network Modeling","year":2007,"lang":"en","type":"article","venue":"","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Montreal Heart Institute","funders":"Fonds de Recherche du Québec - Santé; Genome Canada","keywords":"Single-nucleotide polymorphism; Support vector machine; Gene interaction; Genotype; Artificial neural network; Artificial intelligence; Computer science; Genetics; Computational biology; Gene; Biology; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01229724,0.001003478,0.001842813,0.001744448,0.0003546789,0.0009501948,0.001209054,0.001203241,0.001768012],"category_scores_gemma":[0.03642753,0.0004157499,0.001401712,0.001022259,0.0006617826,0.0009762718,0.00114447,0.001475869,0.0002428551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008310213,"about_ca_system_score_gemma":0.0008106205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004012397,"about_ca_topic_score_gemma":0.002258623,"domain_scores_codex":[0.9931636,0.005490959,0.0002497475,0.0004217779,0.0004930604,0.0001807703],"domain_scores_gemma":[0.9440316,0.0527547,0.001055859,0.0009820348,0.000904059,0.0002718342],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001001304,0.0002122504,0.01441302,0.00007928285,0.0003167853,0.0001452487,0.0000533946,0.9298483,0.001065438,0.004874326,0.0004765967,0.04751406],"study_design_scores_gemma":[0.00001022453,0.00003144718,0.0005852116,0.00000182301,0.000005105776,0.000007503094,0.000002608464,0.9974643,0.0001777082,0.001681489,0.00002866397,0.000003877426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2320181,0.0003262333,0.7650884,0.0003997338,0.00004843719,0.0001518038,0.0003211075,0.0008181779,0.0008279352],"genre_scores_gemma":[0.865401,0.00007983646,0.132926,0.00008715746,0.00003262413,0.0002632063,0.0003671279,0.0000474763,0.0007956754],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01229724,"threshold_uncertainty_score":0.06503481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02054761071987458,"score_gpt":0.2689548858194276,"score_spread":0.248407275099553,"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."}}