{"id":"W2513637016","doi":"10.1097/j.pain.0000000000000694","title":"A data science approach to candidate gene selection of pain regarded as a process of learning and neural plasticity","year":2016,"lang":"en","type":"article","venue":"Pain","topic":"Pain Mechanisms and Treatments","field":"Medicine","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Functional genomics; Chronic pain; Set (abstract data type); Genomics; Gene; Neuroplasticity; Candidate gene; Selection (genetic algorithm); Neuroscience; Computer science; Psychology; Computational biology; Artificial intelligence; Machine learning; Bioinformatics; Biology; Genome; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01177705,0.001051591,0.001878053,0.007429508,0.001414313,0.002865519,0.002076231,0.0013626,0.003003849],"category_scores_gemma":[0.0412421,0.000560823,0.00336274,0.004622907,0.002206054,0.001453196,0.001805314,0.002327531,0.0003346379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001384504,"about_ca_system_score_gemma":0.002517749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002321535,"about_ca_topic_score_gemma":0.002659101,"domain_scores_codex":[0.9926314,0.004769483,0.0004322009,0.00104587,0.000974083,0.0001470298],"domain_scores_gemma":[0.9647824,0.03161673,0.000880491,0.001371804,0.001054855,0.0002936957],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002228974,0.001313064,0.06665912,0.002052027,0.003059582,0.003350772,0.001347363,0.3198916,0.01529841,0.181919,0.006104485,0.3967756],"study_design_scores_gemma":[0.0002081879,0.0002602981,0.005944,0.0000892325,0.0003191649,0.000403233,0.0002592584,0.8279778,0.002472421,0.1582614,0.00374713,0.00005793593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02988514,0.0002930573,0.9662116,0.001108171,0.00006805917,0.000313518,0.0007210834,0.0004630244,0.0009365248],"genre_scores_gemma":[0.2926376,0.0002155205,0.7032356,0.0006038793,0.0001428259,0.001080794,0.001479887,0.0000859904,0.000517901],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01177705,"threshold_uncertainty_score":0.06228369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02489521214810062,"score_gpt":0.2986081329884069,"score_spread":0.2737129208403063,"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."}}