{"id":"W7038172029","doi":"","title":"Identification of variants, genes and pathways in synucleinopathies using bioinformatics and machine learning","year":2023,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University Health Centre","funders":"","keywords":"Identification (biology); Synucleinopathies; Gene; Glycobiology","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":[0.001249662,0.0005067661,0.0006749555,0.00303315,0.0004998783,0.001240472,0.0005974996,0.0003970912,0.003131814],"category_scores_gemma":[0.002438029,0.0002262917,0.001933093,0.002205102,0.0002087065,0.0003697995,0.0004839471,0.0009692323,0.001636531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003798156,"about_ca_system_score_gemma":0.0009822156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002414546,"about_ca_topic_score_gemma":0.004415011,"domain_scores_codex":[0.9995251,0.00008538846,0.00004757725,0.0001916195,0.0001028387,0.00004759382],"domain_scores_gemma":[0.9990417,0.0005393842,0.0001404004,0.00007703521,0.0001327356,0.00006863163],"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":[0.001105102,0.0006594009,0.3143401,0.002029095,0.002060385,0.003189,0.0005660226,0.03299749,0.03117512,0.01062291,0.04954884,0.5517065],"study_design_scores_gemma":[0.0003721465,0.0008025443,0.3313585,0.0009029364,0.002012726,0.005297891,0.0008928949,0.4229711,0.02968012,0.07129434,0.1341643,0.0002506681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.45946,0.01680464,0.3779811,0.005961763,0.0007596755,0.0009099255,0.1082906,0.01742316,0.01240898],"genre_scores_gemma":[0.5300053,0.006345268,0.3840623,0.001036495,0.0002966091,0.0007213303,0.07175337,0.0006285443,0.005150729],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003131814,"threshold_uncertainty_score":0.01047695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.025133180793288,"score_gpt":0.2408158644795949,"score_spread":0.2156826836863069,"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."}}