{"id":"W4389447893","doi":"10.1212/wnl.88.16_supplement.p1.004","title":"PROTEOME-SCALE MOLECULAR NETWORKS MECHANISTICALLY LINK ALPHA-SYNUCLEIN TO DIVERSE GENETIC RISK FACTORS FOR PARKINSONISM (P1.004)","year":2017,"lang":"en","type":"article","venue":"Neurology","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Parkinsonism; Proteome; Alpha-synuclein; Alpha (finance); Link (geometry); Neuroscience; Computational biology; Biology; Genetics; Bioinformatics; Medicine; Computer science; Disease; Internal medicine; Parkinson's disease; Clinical 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001934021,0.0002903579,0.000161753,0.000345508,0.0002098725,0.0004654699,0.0001595087,0.0002415136,0.001633523],"category_scores_gemma":[0.0004876379,0.0001525986,0.0002938361,0.000398625,0.0002661232,0.0004356889,0.0002791955,0.0002973415,0.0001944325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003742793,"about_ca_system_score_gemma":0.000279651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001217479,"about_ca_topic_score_gemma":0.001503041,"domain_scores_codex":[0.9999424,0.00001438653,0.000002360005,0.00002340808,0.00001081279,0.00000651632],"domain_scores_gemma":[0.9998662,0.00005485741,0.00004672457,0.00001010951,0.000009197235,0.00001280996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001082474,0.000461746,0.1338198,0.0007116013,0.000775213,0.001689275,0.0003431981,0.286458,0.411498,0.06894924,0.004731791,0.08947965],"study_design_scores_gemma":[0.000102497,0.0003904098,0.2345676,0.00006382303,0.0004867007,0.0009460843,0.0002806881,0.5219653,0.07542405,0.1521912,0.01352363,0.00005803799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9507419,0.0007738323,0.04069307,0.0006910052,0.00001949446,0.00003183104,0.001853743,0.0004983842,0.004696718],"genre_scores_gemma":[0.9912061,0.0005548691,0.006768003,0.00007568016,0.000005751491,0.00001882521,0.0008318365,0.00001862041,0.0005202247],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001633523,"threshold_uncertainty_score":0.005464673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009467811002551074,"score_gpt":0.2310461525267963,"score_spread":0.2215783415242452,"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."}}