{"id":"W2884672152","doi":"10.1201/b17306","title":"Computational and Visualization Techniques for Structural Bioinformatics Using Chimera","year":2014,"lang":"en","type":"book","venue":"","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Python (programming language); Chimera (genetics); Visualization; Scripting language; Computer science; Computation; Structural bioinformatics; Computational biology; Bioinformatics; Computational science; Data mining; Biology; Algorithm; Programming language; Protein structure; Genetics; Biochemistry","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":[0.002185668,0.001880977,0.001727624,0.002359929,0.001440202,0.004614993,0.005307337,0.001916883,0.0907456],"category_scores_gemma":[0.003742834,0.002657061,0.003265921,0.003776095,0.00117191,0.005944991,0.004026584,0.007987008,0.06717057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001712236,"about_ca_system_score_gemma":0.001767627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002791213,"about_ca_topic_score_gemma":0.002916459,"domain_scores_codex":[0.9984978,0.0002167913,0.0001501938,0.000201183,0.0008289081,0.0001050352],"domain_scores_gemma":[0.9986067,0.0005089383,0.00007221491,0.0003707062,0.0003073391,0.0001339672],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008623031,0.00006398687,0.0001797523,0.001517656,0.0001558767,0.0003254568,0.0003925325,0.006478962,0.01665821,0.1046462,0.635814,0.2336812],"study_design_scores_gemma":[0.00003279498,0.0000125712,0.000139898,0.0001944581,0.00002605911,0.0007391602,0.00003353493,0.01454564,0.008801966,0.04740471,0.9279926,0.00007651839],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004453372,0.006008619,0.909577,0.001174908,0.0007485482,0.0001684234,0.004033652,0.05305058,0.02479294],"genre_scores_gemma":[0.004228911,0.009037453,0.9025214,0.001003134,0.0002325611,0.0006055504,0.01066478,0.02391365,0.04779258],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0907456,"threshold_uncertainty_score":0.3035743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01909083510337069,"score_gpt":0.3232759670534511,"score_spread":0.3041851319500805,"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."}}