{"id":"W1564075136","doi":"10.1007/978-3-642-04241-6_4","title":"Quantifying Systemic Evolutionary Changes by Color Coding Confidence-Scored PPI Networks","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Probabilistic logic; Multicellular organism; Coding (social sciences); Binary number; Color-coding; Enhanced Data Rates for GSM Evolution; Confidence interval; Artificial intelligence; Theoretical computer science; Mathematics; Statistics; Biology; Genetics; Arithmetic","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":[0.001345193,0.0005407868,0.000532613,0.002946499,0.0003415831,0.001563356,0.00103395,0.0009191818,0.00198427],"category_scores_gemma":[0.007340377,0.0003655125,0.0004854748,0.003497145,0.0007001137,0.00134828,0.0008946003,0.0009581535,0.0004475424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006616567,"about_ca_system_score_gemma":0.0002596767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001285255,"about_ca_topic_score_gemma":0.002570617,"domain_scores_codex":[0.9990911,0.0001799709,0.00003594694,0.0002718882,0.0003348539,0.00008615455],"domain_scores_gemma":[0.9954092,0.002442997,0.0007750553,0.0005008538,0.0006640729,0.0002078345],"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":[0.0008566143,0.0001779881,0.1078857,0.0005041533,0.0005872682,0.0004595554,0.0002879881,0.188783,0.2794346,0.0224358,0.00382418,0.394763],"study_design_scores_gemma":[0.00002793403,0.0001295007,0.09387401,0.00003111934,0.000126309,0.0006705914,0.0001015969,0.83895,0.03634496,0.02797256,0.001676783,0.00009457346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5901578,0.0006645812,0.4010514,0.0001858005,0.00006879832,0.00004311399,0.001550744,0.001330091,0.004947741],"genre_scores_gemma":[0.9102744,0.0002141234,0.08669946,0.00004273007,0.00003400914,0.00003160263,0.001404431,0.0002085656,0.001090701],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002946499,"threshold_uncertainty_score":0.007114112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01744083770156633,"score_gpt":0.2382859820801258,"score_spread":0.2208451443785595,"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."}}