{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003168437,0.00006907165,0.0001580646,0.00009231095,0.00005718436,0.000005087754,0.00008728696,0.0000230822,0.000008282342],"category_scores_gemma":[0.003483983,0.00004384095,0.000009809113,0.0002484872,0.00007240817,0.00007743212,0.00005778182,0.000042092,7.535823e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002579035,"about_ca_system_score_gemma":0.0001072718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009096393,"about_ca_topic_score_gemma":0.00001276201,"domain_scores_codex":[0.9990386,0.0002049817,0.0001252333,0.0002651515,0.0002151689,0.0001508477],"domain_scores_gemma":[0.9994424,0.0001795344,0.0000716901,0.0001261677,0.00008299161,0.00009720421],"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":[0.0004516274,0.0003272075,0.1178186,0.0003145969,0.00006580924,0.000006599737,0.0007280794,0.00003117768,0.8164325,0.0001327207,0.00006065743,0.06363047],"study_design_scores_gemma":[0.005609456,0.004347777,0.03542096,0.0009049166,0.0002614167,0.0001110051,0.001097363,0.3938046,0.5570446,0.0004766427,0.0005102907,0.0004109936],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.897862,0.00002080078,0.1014826,0.0001061679,0.000009389712,0.0002546514,0.00001411491,0.00001774897,0.0002324763],"genre_scores_gemma":[0.9955388,0.000007030464,0.00423378,0.00004829288,0.00001484,0.00001121124,0.000008266695,0.000006457397,0.0001312849],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3937735,"threshold_uncertainty_score":0.4170905,"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."}}