{"id":"W2474759991","doi":"","title":"In Silico Investigation of Traditional Chinese Medicine for Potential Lead Compounds as SPG7 Inhibitors against Coronary Artery Disease","year":2016,"lang":"en","type":"article","venue":"DSpace@MIT (Massachusetts Institute of Technology)","topic":"Kruppel-like factors research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Traditional medicine; Medicine; Coronary artery disease; In silico; Disease; Lead (geology); Internal medicine; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0004505026,0.00135589,0.001916071,0.001069942,0.0005747823,0.0009722942,0.0008499183,0.0006519287,0.007317651],"category_scores_gemma":[0.0007354274,0.0004054302,0.001752007,0.0008112936,0.0002208254,0.0004539904,0.0004980977,0.0004620243,0.0006002465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005974438,"about_ca_system_score_gemma":0.001585903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006258258,"about_ca_topic_score_gemma":0.009253134,"domain_scores_codex":[0.9998348,0.00004465715,0.000009460841,0.00003271658,0.00003943879,0.0000389374],"domain_scores_gemma":[0.9997026,0.0001898211,0.00003239899,0.00000995736,0.00003690629,0.00002825869],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001111468,0.0005021876,0.01512214,0.001484916,0.0007368689,0.001417806,0.00006036614,0.9427058,0.01271298,0.00445878,0.003767038,0.01591977],"study_design_scores_gemma":[0.0002776517,0.000858986,0.001652134,0.00005223937,0.0004738388,0.0001561277,0.00006728284,0.9887205,0.003039294,0.001314331,0.003364227,0.00002348022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9403793,0.006821982,0.01971997,0.001122487,0.0001642437,0.0004499072,0.007100273,0.001128406,0.02311343],"genre_scores_gemma":[0.9664168,0.002484604,0.02065206,0.0003756048,0.00003564366,0.0003087556,0.00628028,0.00009981204,0.003346476],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007317651,"threshold_uncertainty_score":0.02448004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01663732507637928,"score_gpt":0.2672437902822052,"score_spread":0.2506064652058259,"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."}}