{"id":"W3106442513","doi":"10.1101/470757","title":"Identification of biological mechanisms underlying a multidimensional ASD phenotype using machine learning","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Autism Spectrum Disorder Research","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundação para a Ciência e a Tecnologia; National Institute of Mental Health; Medical Research Council; Genome Canada; Canadian Institutes of Health Research; Autism Speaks","keywords":"Autism spectrum disorder; Phenotype; Genetic architecture; Autism; Copy-number variation; Computational biology; Cognition; Biology; Psychology; Gene; Genetics; Neuroscience; Genome; Developmental psychology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002257194,0.000752971,0.0007953744,0.002758761,0.00029411,0.001312171,0.0004543168,0.0007090177,0.0008659794],"category_scores_gemma":[0.004383034,0.0001987883,0.0008823268,0.001165061,0.0005101718,0.0005655411,0.0008092927,0.0008426247,0.0002995255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005374738,"about_ca_system_score_gemma":0.0004108403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001024731,"about_ca_topic_score_gemma":0.000807422,"domain_scores_codex":[0.9988773,0.0004727913,0.000103806,0.0002973032,0.0001726177,0.00007600984],"domain_scores_gemma":[0.9968363,0.002119232,0.0004626047,0.000284064,0.0001930014,0.0001048129],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008757011,0.0007689495,0.6470236,0.0003878581,0.001410916,0.0008376336,0.0003246136,0.09166957,0.04778364,0.003332607,0.002206847,0.203378],"study_design_scores_gemma":[0.00003491952,0.0002073744,0.2026581,0.00007932104,0.0001376446,0.0004894649,0.0001651472,0.7755426,0.006694549,0.01322462,0.0007227276,0.00004345655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8307313,0.001140357,0.1641507,0.0006999045,0.00003227707,0.000111156,0.001493228,0.0005612542,0.001079602],"genre_scores_gemma":[0.9757681,0.0000953708,0.02312091,0.00005910638,0.00001928781,0.00003402516,0.0007798966,0.00001068801,0.000112733],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002758761,"threshold_uncertainty_score":0.01193732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07683221277865031,"score_gpt":0.3051295361753141,"score_spread":0.2282973233966638,"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."}}