{"id":"W2971696794","doi":"10.1371/journal.pone.0220995","title":"A systems biology approach towards the identification of candidate therapeutic genes and potential biomarkers for Parkinson’s disease","year":2019,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Parkinson's Disease Mechanisms and Treatments","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Pars compacta; Substantia nigra; Candidate gene; Disease; Parkinson's disease; Biology; Gene; Gene expression profiling; Dopaminergic; Bioinformatics; Dopaminergic pathways; Biomarker; LRRK2; Computational biology; Gene expression; Neuroscience; Medicine; Genetics; Pathology; Dopamine","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":[0.001970785,0.0007274924,0.001326735,0.0008348504,0.0005502729,0.002027128,0.0007385925,0.0007519542,0.001411195],"category_scores_gemma":[0.001485152,0.0004749195,0.001116236,0.0005785736,0.001471221,0.0007704635,0.0008025714,0.001349032,0.0003299495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001317097,"about_ca_system_score_gemma":0.001634922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006527033,"about_ca_topic_score_gemma":0.0007884883,"domain_scores_codex":[0.9992261,0.0003373665,0.00004461277,0.000210574,0.0001338212,0.0000474695],"domain_scores_gemma":[0.9994153,0.0003414536,0.00007757397,0.00007951908,0.00005943023,0.00002678227],"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":[0.0007329904,0.0005541766,0.006855958,0.001839219,0.000522822,0.000552188,0.00030304,0.04157384,0.7403154,0.0947248,0.001644073,0.1103814],"study_design_scores_gemma":[0.0004454879,0.005333022,0.01209765,0.0003029379,0.001275595,0.001294107,0.0004534597,0.3350997,0.4197689,0.1681053,0.05561617,0.0002077237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1252032,0.01057411,0.8468711,0.004189637,0.0004609069,0.0009717185,0.001006015,0.001007802,0.009715504],"genre_scores_gemma":[0.5469458,0.006975519,0.4386518,0.001549297,0.0001558983,0.001709905,0.0007015323,0.00006345213,0.003246915],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002027128,"threshold_uncertainty_score":0.01042265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0424528536018236,"score_gpt":0.2582480021781907,"score_spread":0.2157951485763671,"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."}}